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C2020-011 - IBM SPSS Statistics Level 1 v2 - BrainDump Information

Vendor Name : IBM
Exam Code : C2020-011
Exam Name : IBM SPSS Statistics Level 1 v2
Questions and Answers : 55 Q & A
Updated On : January 21, 2019
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C2020-011 exam Dumps Source : IBM SPSS Statistics Level 1 v2

Test Code : C2020-011
Test Name : IBM SPSS Statistics Level 1 v2
Vendor Name : IBM
Q&A : 55 Real Questions

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IBM IBM SPSS Statistics Level

IBM Wins a 2018 crimson Dot Design Award for SPSS statistics | killexams.com Real Questions and Pass4sure dumps

The IBM Hybrid Cloud team is again at it with yet one more win for design. I’m excited to announce that our design crew has been awarded the 2018 pink Dot: communique Design Award for IBM SPSS records in the Interface Design class. This award is a continuation of the design achievements we have viewed this past year, together with the A’Design Awards, IF Design Awards, and others. i am extremely joyful to see the hard work of our designers and IBM Design proceed to shine and make a change in business software.

First developed within the 1990’s, the pink Dot Award has been the revered overseas seal of fantastic design satisfactory. Designers, organizations, and companies from forty five distinct nations took part during this year’s competition, totaling over eight,600 entries that underwent a 24 member jury.

“All folks that growth through the hard adjudication process to garner a red Dot have every purpose to be proud of themselves, because the jury offers our award handiest to creations of excessive design excellent. This makes me all the more delighted to congratulate the laureates sincerely on their genuinely-earned success.” — Professor Dr. Peter Zec, founder and CEO of the purple Dot Award

Receiving this award changed into highly exciting for our crew and we are honored to be among the many winners. here's an incredible fulfillment for our designers who labored on this product, and they faced an interesting and difficult event in working on this product.

what's IBM SPSS?

IBM SPSS statistics is a powerful records analysis tool that is one of the most wide-spread records purposes. for the reason that its inception in 1968, SPSS statistics has been revamped and redeveloped distinct times. Now the design team at IBM has taken on the project of creating a totally sparkling consumer journey.

during this newest redesign of IBM SPSS information, we carried out design pondering ideas via working intently with our clients and making certain this modernized version of SPSS statistics aligns with their needs. Our top-quality intention became to create a powerful tool that isn't handiest effortless and intuitive to make use of, however that our clients can take pleasure in.

Our group and Design strategy

The IBM SPSS design crew is a part of the IBM Design Studios in Boeblingen, Germany. The crew is composed of a various neighborhood, with many members originating from distinct nations and cultures. Some individuals of the team had some background with facts while others were working during this box for the primary time.

Following the principles of IBM Design pondering (have a look at > reflect > Make), our team carried out a redecorate that brings a more robust focal point on clients for SPSS data. The design team conducted intensive analysis on the user base of SPSS data as a way to see how the software can stronger meet their wants. The existing user base ranges from less skilled clients similar to college students to greater expert clients corresponding to facts scientists or enterprise specialists. A key perception from the group’s research was that less skilled clients had been intimidated each by the mathematics work and the complexity of the application.

the new designs concentrated on simplifying workflows, cutting back the overall complexity of the UI and interactions, and presenting novices a straightforward on-boarding to information and to the product. a further critical feature in the redesign became a practicing e book led by using a personality named Simon, who serves as an in-application e-book, helping newbie users take note different features and achieve their desires faster.

The team confronted some pleasing challenges in redesigning a fabricated from such complexity, and one that has additionally been around for thus decades. a large success of the designers changed into making the product accessible and tasty to new clients with out alienating decade-long, skilled users.

a look Into the Future

The preview version of our new IBM SPSS data journey was released in March 2018, and made purchasable to the general public as a trial on the IBM believe conference is Las Vegas, and considering that June 26 , the brand new UI is commonly attainable to all SPSS facts subscribers. This preview is only the preliminary step, providing probably the most used statistical analyses, and primary capabilities for information guidance, for presentation and for reporting results. Over right here months the team should be working so as to add greater points and capabilities with a view to meet adventure wants of all of our user corporations.

no longer simply Updating — Redesigning

i am so thrilled to see a further Hybrid Cloud design team acquire a world award for his or her work. IBM SPSS statistics is yet one more example of how design is making a big change within the success of our items. As we proceed to make use of design to create extra relatable and effective items, we're capable of give our clients the experiences that they need and wish. I’m extremely joyful and proud to monitor the change that our design group is making on the earth of enterprise application, and that i can’t wait to peer how we continue to influence the lives of our clients.

Award Winners:
  • Design manager: Caroline legislations
  • Design Leads: Dirk Willuhn and Eva Cochet-Weinandt
  • Design team: Christian Fritsche, Dimitri Hoffmann, Jaehee (Chloe) Lee, Oleksandr Sabov, Stephan Feger
  • due to these contributing designers: Katrin Ellice Heintze, Leila Johannesen, Marion Bruells, Phil Brucker, Robin Auer, Sammy Schuckert, Stefan Schwarz
  • Design interns: Mengzhu Deng, Nathalie Mader, Ting-Hao (Howard) Huang, Vanessa Ng

  • My Highlights from IBM suppose 2018: statistics Science, SPSS, Augmented fact and the consumer experience | killexams.com Real Questions and Pass4sure dumps

    I attended IBM’s inaugural suppose adventure in Las Vegas closing week. This event, IBM’s greatest (estimated 30,000+ attendees!), concentrated on making your enterprise smarter and blanketed keynotes and classes on such topics as synthetic intelligence, statistics science, blockchain, quantum computing and cryptography. i used to be invited by using IBM as a visitor to share some insights from the standpoint of a data scientist. below are a few highlights of the adventure.

    information Science using IBM SPSS SPSS at 50

    50 Years of SPSS Innovation. click photograph to enlarge.

    IBM SPSS is IBM’s set of predictive analytics items that address the complete analytical technique, from planning to statistics collection to analysis, reporting and deployment. IBM celebrated the 50th anniversary of IBM SPSS with their new beta free up of IBM SPSS records 25, the greatest beta free up in its heritage. The up to date edition includes new trends like ebook-able charts, MS workplace integration, Bayesian facts and superior statistics. also, they added a new person interface which is pretty slick.

    i was introduced to SPSS facts in faculty and have used it for each one of my research tasks due to the fact then. To be sincere, SPSS facts has aged greater than I actually have! I even have already started using the new version and am fairly excited about the new aspects and consumer interface. i'll record about event in a later post. try SPSS with a free 14-day trial.

    improving the customer experience

    contemporary experiences have estimated that forty five% of agents are anticipated to raise using synthetic intelligence for consumer journey in the subsequent three years, and fifty five% of agents are concentrated on optimizing the client event to enhance consumer loyalty. additionally, 85% of all client interactions with a company might be managed with out human interplay by means of 2020.

    customer adventure administration (CXM) is the system of knowing and managing purchasers’ interactions with and perceptions about the company/manufacturer. IBM knows that improving the client event is more and more becoming records-intensive exercise, and the usage of the mixed power of statistics and these days’s processing capabilities can aid businesses model the methods that influence the customer journey. I attended a few periods to find out about how IBM is leveraging the energy of IBM Watson to help their purchasers with Watson Commerce and Watson client experience Analytics options. These solutions use the vigour of artificial intelligence (e.g., predictive analytics) to increase how groups can greater manipulate customer relationships to boost consumer loyalty and stream their business forward.

    facts Science Meets better Analytics and Augmented truth

    These facts professionals from Aginity, IBM Analytics, H2O.ai and IBM Immersive Insights are enhancing how you get from records to insights.

    I saw a pretty good demonstration of the intersection of records science, improved analytics and augmented truth. Getting from statistics to insights is the intention of records science efforts and, as facts sources proceed to develop, we are able to want superior the right way to get to those insights. Aginity is working with H2O.ai to exhibit the best way to enhance your predictions through augmenting public statistics with enhanced statistics (with derived attributes) and enhanced analytics to make more suitable predictions. the usage of baseball data, Ari Kaplan of Aginity brought up that the advancements in predictive fashions may translate into hundreds of thousands of dollars per participant. while his demo concentrated on the use of those technologies in baseball records, the principles are generalizable to any business vertical, including finance, healthcare and media.

    at the identical demonstration station, Alfredo Ruiz, lead of the Augmented reality program at IBM Analytics, confirmed me how his crew (IBM Immersive Insights) is incorporating augmented reality into information Science journey to assist businesses enhanced bear in mind their ever-expanding records sets. I’m anticipating seeing how his efforts in marrying augmented truth and facts science growth.

    I had the privilege of interviewing Ari Kaplan of Aginity who talked about the work he is doing to enhance how Aginity and H2O.ai is enhancing the records science technique. try what he has to say below.

    Don’t pass over this interview with Ari Kaplan, a true “Moneyball” and smartly regular round most important League Baseball, as he talks in regards to the latest machine getting to know technologies powering these days’s baseball selections, and take a look at the great demo.

    Posted through IBM statistics Science on Thursday, March 22, 2018

    statistics Science is a group recreation

    Bob, Al and Dez. graphic via Dez Blanchfield

    I had the opportunity to speak with with many trade consultants who come to statistics science from a distinct standpoint than I do. whereas I focus essentially on the facts and mathematics facets of facts science, lots of my data peers method facts science from a technological and programming attitude. basically, for an upcoming podcast, Dez Blanchfield and that i had been interviewed by way of Al Martin of IBM Analytics to discuss our respective roles in facts science. This conversation became a energetic one, and that i am longing for reliving that night once the podcast is released. The base line is that records science requires such a various ability set that you really want to work with different people who can complement your expertise.

    I’m with data professionals (and actors) Trisha Mahoney, Ryan Arbow and Shadi Copty.

    This thought that statistics science is a group sport become placed on full monitor in an pleasing session during which a couples therapist (Trisha Mahoney) helped unravel an argument between an information science leader (Shadi Copty) and IT leader (Ryan Arbow). Asking probing questions, the counselor revealed that the statistics science and IT leader have been at odds because of a lack of communication. She delivered them to IBM’s data Science event, an enterprise facts science platform that makes it possible for them to without problems collaborate, use correct open source equipment and get their models into production sooner.

    Analytics: Your competitive expertise

    For me, IBM believe 2018 became all about making your company smarter via analytics. truly, research suggests that groups that are superior capable of deliver the energy of analytics to bear on their business complications might be in a much better position to outperform their analytics-challenged competitors. This conception was illustrated via keynotes, sessions and conversations. by way of bringing distinctive records science experts collectively to leverage the tools and strategies of AI and computing device/deep studying will support you flow your enterprise forward. in case you had been unable to attend the adventure, that you would be able to watch replays of most of the keynotes here.

    (Disclosure: IBM assisted me with go back and forth expenses to IBM consider 2018.)


    evaluating the main huge information analytics utility alternate options | killexams.com Real Questions and Pass4sure dumps

    there are many vendors selling items labeled as huge information analytics software. despite the fact, it's challenging to...

    differentiate these items in response to performance alone, as many of the equipment share an identical features and capabilities. additionally, one of the crucial tools show extremely subtle adjustments.

    That being referred to, your key differentiating elements will doubtless center of attention on balancing ease of use, algorithmic sophistication and value in terms of your firm's capability and stage of maturity in analytics.

    in this article, we verify products from 9 large records analytics utility companies: Alteryx Inc., IBM, KNIME AG, Microsoft, Oracle, RapidMiner Inc., SAP, SAS Institute Inc. and Teradata Corp. Some of those providers supply more than one device. See the "main vendors of big records analytics utility" sidebar under for more details about their selected product offerings.

    These companies symbolize diverse aspects of the huge statistics analytics market. Let's compare and distinction the ways that these items meet the business wants of user companies.

    Analyst capabilities and expertise 

    Some statistics analytics tools are targeted to amateur clients, some are focused to expert facts analysts and some are engineered to attraction to both forms of clients.

    items comparable to IBM SPSS Modeler, RapidMiner's equipment, Oracle superior Analytics and the automatic Analytics edition of SAP BusinessObjects Predictive Analytics are frequently designed to permit clients with a restrained heritage in statistics or facts analysis to analyze facts, boost analytical models and design analytics workflows with little or no coding.

    while each supplier wraps its core analytics components with an intuitive person interface to ebook the analyst's development in facts instruction, analysis, after which mannequin design and validation, the approach taken may also range, primarily when evaluating a stand-alone product, similar to RapidMiner, with one it is a component of a larger suite, such because the Oracle product.

    tools equivalent to IBM SPSS data, KNIME Analytics Platform, the skilled Analytics module of SAP BusinessObjects Predictive Analytics, Microsoft R and the Teradata Aster Analytics platform give the more sophisticated performance that skilled users expect. Oracle R advanced Analytics for Hadoop (ORAAH), one of the most accessories in the Oracle big statistics application Connectors suite, offers an R interface for manipulating Hadoop disbursed File equipment statistics and writing mapper and reducer services in R. this flexibility may be appealing to extra advanced records scientists.

    Alteryx and SAS business Miner offer performance adapted to the consumer's stage of expertise, and just about fall into both classes. Alteryx has delivered advancements to information profiling to assist records scientists more desirable understand their information sources. usual, SAS business Miner and IBM's SPSS equipment stand out when it involves helping extra superior analytical recommendations and model scoring, in addition to a broader array of evaluation services, including neural networks, affiliation analysis and visualization capabilities.

    Analytical range

    reckoning on the use case and application, your company's clients could be required to support several types of analytics capabilities that will use specific types of modeling, similar to regression, clustering, segmentation, behavior modeling and resolution timber.

    while this has resulted in broad assist for the numerous sorts of analytical modeling at a excessive degree, some carriers have invested many years of work into tweaking distinct models of their algorithms and including greater sophisticated functionality. it's critical to understand which fashions are most significant to your enterprise issues and to evaluate the products in terms of how they premier serve your clients' business needs.

    it be crucial to take note which fashions are most imperative to your company problems and to evaluate the products when it comes to how they foremost serve your users' enterprise wants.

    The extra mature and better-conclusion -- and, thus, better-priced -- equipment will exhibit the finest analytical breadth. Oracle records Miner includes an array of time-honored computing device researching techniques to aid clustering, predictive mining and text mining. each variations of IBM's SPSS product deliver a various set of analytical thoughts and fashions. And SAS enterprise Miner supports many algorithms and options, including choice trees, time sequence, neural networks, linear and logistic regression, sequence and net course evaluation, market basket analysis, and link evaluation.

    The more recent era -- and, in some situations, decrease-priced -- items support distinct fashions, however possibly with a narrower range of algorithmic sophistication.

    The model stock in Alteryx Analytics Gallery includes such capabilities as regression evaluation, choice bushes, association rule evaluation, classification and time sequence evaluation. KNIME contains methods for text mining, graphic mining and time collection evaluation, and additionally integrates laptop getting to know algorithms from other open supply tasks, reminiscent of Weka and JFreeChart.

    one other aspect of analytical range is integration with programming languages and statistical tools, similar to R, for incorporating current libraries, in addition to user-described functionality. really, integration with R could be considered an increasingly essential differentiator.

    Alteryx designer, Microsoft R, SAS commercial enterprise Miner, Teradata Aster Analytics, Oracle's ORAAH and KNIME's Analytics Platform all interface and help integration with R. a couple of of the providers, including IBM, Oracle, Microsoft, RapidMiner and SAP, deliver a transforming into library of extensions to R and Python, enabling users to take competencies of free libraries.

    Scope of the records to be analyzed

    There are diverse aspects of the scope of the statistics to be analyzed, including the problem of structured vs. unstructured information, as well as entry to commonplace on-premises databases and statistics warehouses, cloud-primarily based facts sources, and records managed in big facts systems, equivalent to Hadoop.

    although, there are various levels of aid for records managed inside less-widely wide-spread information lakes -- both managed inside Hadoop or in an extra NoSQL facts management device supposed to supply horizontal scaling. The elements for distinguishing among the items must be according to your corporation's certain requirements for accessing and processing records volumes and statistics diversity.

    In recognition of the growing to be diversity of input sources and the variety of underlying systems used to house these facts sets, another set of rising elements it is being adopted by using these vendors involves facts accessibility. IBM, RapidMiner, Alteryx, Oracle and Microsoft have all more desirable their equipment' facts import, export and connectivity capabilities. These enhancements may still enable clients to entry a more finished listing of statistics sources whereas simplifying and speeding up the technique of loading facts into the products.

    aid for scalability and excessive efficiency

    The need for scalable efficiency is driven by means of your organization's data volumes and urge for food for analysis. Smaller agencies with less records could be able to tolerate items that would not have performance qualities that scale with the accessible components, such because the entry-degree models of the decrease-conclusion equipment, including RapidMiner, KNIME, Microsoft R Open and Alteryx clothier, which can run on desktop techniques and do not require further server components.

    greater companies are more likely to have a more suitable stock of facts units to research, as well as broader communities of users. This introduces two further necessities -- high efficiency and facilitation of collaboration. The adaptability of a product to excessive-efficiency architectures is a fine indication of scalability, and most of the products can also be adapted to the parallelism of Hadoop or make use of some other means of attaining faster computation.

    the entire products do have some guide for Hadoop, together with IBM SPSS Modeler and SPSS information; RapidMiner's industrial element Radoop, which connects the Studio front end and Server analysis engine to statistics kept in Hadoop; Oracle's massive information Discovery and ORAAH tools; and KNIME's large facts Extensions and Cluster Execution add-ins.

    IBM SPSS now additionally provides more advantageous guide for a few multithreaded analytical algorithms that can also pace performance. Teradata Aster Analytics addresses high-performance necessities via its vastly Parallel Processing structure. SAP's knowledgeable Analytics version of SAP BusinessObjects Predictive Analytics can execute in-memory statistics mining for managing big-quantity statistics evaluation effectively. Microsoft R Server leverages its ScaleR module, a comprehensive library of big records analytics algorithms that assist parallelization. Scoring algorithms implemented using SAS enterprise Miner will also be deployed and accomplished within a Hadoop atmosphere.

    furthermore, integration with Apache Spark appears to be of growing value. SPSS, KNIME, Oracle, RapidMiner and SAP all deliver entry to Apache Spark libraries to aid analytics functions that should scale with exploding statistics volumes. This permits developed purposes to take potential of a excessive-efficiency cluster platform to distribute the workflow across the cluster.

    Collaboration

    As stated, the higher the company, the greater doubtless there might be a necessity to share analyses, models and applications across distinctive businesses and among many analysts. companies that have many analysts allotted across the enterprise can also look for expanded potential to share fashions and collaborate related to the interpretation of effects.

    IBM's SPSS Modeler Gold version provides collaboration capabilities, and RapidMiner's Server product offers help for sharing and collaboration. Alteryx Analytics Gallery provides a mechanism for sharing refined analytics applications within the cloud with participants of an extended corporation. KNIME offers industrial extensions to aid team collaboration, in addition to extensions assisting operational collaboration, similar to far off-scheduled execution, file generation, shared records space and a workflow repository. SAS enterprise Miner's client-server architecture allows business users and records analysts to work collaboratively with the aid of sharing models and different work products.

    Alteryx, KNIME and Teradata Aster have added capabilities to aid manipulate analytical workflows. additionally, one of the most companies have all started to look at how to allow their tools to integrate with others that may additionally have complementary practical sweet spots. for example, Teradata Aster now has an extension to integrate with KNIME that allows for clients to leverage the KNIME workflow editor and include Aster Analytics capabilities into those workflows.

    dealer measurement and product integration

    vendors will also be compared when it comes to their measurement. One might evaluate and contrast what may well be observed because the mega-providers, whose huge statistics analytics tools are only one product amongst a enormous portfolio of equipment. if you work for a bigger firm that typically negotiates web site-vast, business licenses for the whole suite of a vendor's equipment from a mega-dealer reminiscent of IBM, SAS, SAP or Oracle may well be an inexpensive alternative.

    The significant companies promote big statistics analytics tools that are a part of a a great deal bigger device ecosystem. presumably, the items from a mega-vendor may be as a minimum just a little integrated and meant to work collectively. additionally, some individuals believe greater comfortable with greater companies, with an expectation of steadiness and constant consumer provider. having said that, you may additionally only be able to purchase these massive information analytics equipment as a part of a an awful lot greater software licensing association.

    Smaller vendors, such as KNIME, Alteryx and RapidMiner, have revenues that are often based on licensing and assist for a small number of large statistics analytics products. A smaller dealer may deliver closer contact with their product administration and innovation teams, and you can be able to have an effect on the direction of the product roadmap or better functionality.

    A smaller vendor could even be extra flexible when it comes to price and the features protected in the licensing arrangement. You must realize, despite the fact, that working with a smaller vendor does existing some chance in terms of stability, the supplies accessible for guide and the possibility that the enterprise can be received, that may impact the customer relationship.

    The greater carriers are clearly conscious of consumer wants for integration with other methods, despite the fact that regularly facilities on different products inside each and every vendor's stock. as an instance, SAP Predictive Analytics has more suitable integration with SAP HANA and BusinessObjects Cloud. SAS business Miner has brought nodes to execute code in a SAS open, cloud-capable, in-memory Viya atmosphere. Microsoft presents SQL Server R functions, an R setting up that runs alongside SQL Server and allows for users to combine Microsoft R Server statistics with SQL Server and Microsoft's other business intelligence tools.

    price range for licensing and preservation

    very nearly all of the carriers promote distinct types or variations of their items, with quite a number costs for acquisition and complete cost of operation. IBM, Oracle, RapidMiner, Teradata and Microsoft sell editions at distinct tiers, with the license cost proportional to the facets, capabilities and freedom from boundaries when it comes to the volumes of facts to be analyzed or the number of processing nodes the product can use.

    KNIME and RapidMiner give free and open supply types of their products, either charging for assist features or for variants supporting enterprise-category applications. KNIME, RapidMiner and Alteryx have highly low licensing costs for a smaller number of clients. when you are for the reason that SAS or SAP, you ought to contact them for pricing options.

    The industry for big records analytics utility may also be a confusing place, but with a bit of luck this article has helped you take into account the benefits big data analytics software can provide your organization, and assisted you in differentiating between the selected tools examined here.




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    The association between the parental perception of the physical neighborhood environment and children’s location-specific physical activity | killexams.com real questions and Pass4sure dumps

    The main aim of this study was to investigate the association between parental perceived physical environmental characteristics of the neighborhood and children’s location-specific PA. Furthermore, the association between children’s physical neighborhood environment and their overall MVPA was investigated. As expected, physical neighborhood environmental correlates of children’s PA varied by PA location and perceived physical neighborhood characteristics were unrelated to children’s overall MVPA.

    The presence of neighborhood recreation facilities was the most important condition for children to be active in public recreation spaces that were located in- or outside their neighborhood. This finding may imply that most reported PA in public recreation spaces took place in facilities that were located inside children’s neighborhood. All other perceived neighborhood characteristics were unrelated to PA in public recreation spaces that took place inside or outside the neighborhood. Proximity to recreational facilities may promote children’s activity in these facilities, as recreation facilities nearby children’s home are better accessible for children compared to recreational facilities outside the neighborhood. This indicates that intervention developers have to focus on the presence of these facilities, rather than focusing on e.g. the aesthetics along the road to these facilities, as aesthetics were unrelated to PA in recreation facilities. In a US study, small public parks, playgrounds, playfields/courts and large public parks were among the five most commonly used PA sites for children; and children were more active in smaller parks compared to larger parks [18]. This may indicate that providing sufficient public recreation spaces for children can possibly yield positive effects on children’s PA and that the presence of smaller parks nearby can be more effective in increasing PA than larger parks that are further away from children’s home. However, the present results should be interpreted with caution because reverse causality may be present. For example, it is possible that parents from children who are frequently active in a public recreation space are more aware of these facilities, compared to parents from children who are mostly active in the garden.

    It is possible that not only the presence of recreation facilities is important to explain children’s PA in these facilities, but also the presence of features in the recreation facilities and their quality may play an important role in relation to children’s PA. For example, in an Australian study, park improvements (including the establishment of a walking track, a barbecue area, a playground,..) were positively associated with the number of park users, the number of people observed walking and being vigorously active [41]. Also in the US, park renovations appeared to increase visitation and overall PA in different age groups [42]. Future research is necessary to investigate if correlates of PA in public recreation spaces inside the neighborhood differ from correlates of PA in public recreation spaces outside the neighborhood.

    Parental perceived land use mix accessibility and crime safety were positively associated with PA in nearby streets and on sidewalks. In another Belgian study that investigated the correlates of children’s active commuting to school, land use mix accessibility was also positively related to children’s active transport to school [43]. These findings may indicate that a neighborhood with a high perceived accessibility is important for children to be active in their neighborhood. The positive relation between crime safety and PA in nearby streets and on sidewalks was expected as safety concerns may cause parents to restrict their children to play outdoors [44]. Also in an Australian study, parental perceptions of safety were positively related to children’s play in their street [20]. A negative association was found between street connectivity and PA in nearby streets and on sidewalks. This negative association with street connectivity can be explained by the fact that a neighborhood with low connectivity is characterized by few intersections and more cul-de-sacs that reduce traffic volume, which results in safer places to play in the streets. The negative association between street connectivity and reported child activity in the neighborhood was also found in a US study [15] and shows that an activity friendly neighborhood for children differs from an activity friendly neighborhood for adults. In adult studies it has consistently been shown that a higher street connectivity is associated with more PA [45, 46]. The challenge for urban planners and policy makers is to develop a neighborhood in which people from different age groups are encouraged to be physically active. For example, this can be done by providing sufficient play space (e.g. small parks) in neighborhoods with a high street connectivity for walking and cycling.

    In contrast to our hypothesis that an activity unfriendly neighborhood would be associated to more garden PA, but similar to the results of an Australian study [20], none of the perceived neighborhood environmental factors were related to children’s PA in the garden. Based on these findings, it is assumed that other factors (e.g. family environmental factors such as number of siblings, parental rules, parental encouragement) explain children’s PA in the garden and that intervening in children’s neighborhood environment will not influence children’s garden PA. However, further research is necessary as it is possible that specific garden characteristics (e.g. size of the garden) mediate the association between neighborhood characteristics and children’s garden PA.

    These findings show that the physical neighborhood environment is mainly related to PA that actually takes place in children’s neighborhood (in nearby streets and on sidewalks) and is probably unrelated to PA in other contexts. This possibly explains the fact that the neighborhood physical environment was unrelated to children’s overall MVPA. As a large part of children’s overall MVPA takes place outside the neighborhood (e.g. in the sports club or at school) and only a small part of their overall PA takes place in the neighborhood or public recreation spaces, the influence of the neighborhood physical environment on children’s overall MVPA might be limited; whereas in adults, the neighborhood physical environment relates to overall MVPA in adults [47]. Also in an Australian study, the frequency children played in specific outdoor locations (i.e. their own street, their garden and in the park/playgrounds) was unrelated to overall MVPA [20]. However, in the present study, overall MVPA was measured during the school year. It is possible that the perceived neighborhood environment relates more strongly to overall MVPA during school vacations, because then children have less opportunities to be active at schools or in a sports clubs.

    More insight into the location-specific PA correlates will be very informative for policy makers or urban planners, aiming to increase children’s PA levels in specific places (e.g. recreation facilities). Therefore, in future studies the use of GPS and/or SenseCams (wearable camera that takes photos automatically) in combination with accelerometers are promising tools for investigating the association between the environment and children’s location-specific PA. By using the combination of GPS and/or SenseCams and accelerometers, children’s PA can be exactly located in the neighborhood and data will not be biased by self-report. Also the use of activity diaries in combination with accelerometers might provide valuable information (e.g. where the activity took place) to investigate the relation between the perceived neighborhood environment and overall MVPA in specific locations. In future research, also the relation between the perceived neighborhood environment and objectively measured MVPA during vacation and other specific time periods (e.g. critical window MVPA (=after school until 6 pm)) should be investigated.

    Strengths of this study were the use of the validated NEWS, the most commonly used questionnaire in the literature to assess environmental perceptions [48], the relatively large sample, the use of accelerometry to objectively determine MVPA and the use of parental perceptions of the physical environment. The cross-sectional study design is a limitation, as no causal relationships could be examined. Furthermore, no objective measures of location-specific PA were available which made it impossible to test the criterion validity of these measures. Also the neighborhood characteristics were measured by self-report. It is possible that correlated error might have influenced the association between the two self-reported measures (i.e. parental reported PA and parental reported neighborhood characteristics) to a small extent. Also the response rate of the principals was rather low, which may have limited the representativeness of the findings. For example, it is possible that the present results are not generalizable to children from schools with a lower SES, as participating schools had a slightly higher number of children with high SES compared to other schools in Ghent (e.g. 27.0 % of children’s mothers did not obtain a secondary education degree in participating schools versus 32.6 % in non-participating schools in Ghent). Besides, 7.0 % of the parents did not fill out the questionnaire after giving informed consent which can have resulted in a selection bias.


    Associations of ADL and IADL disability with physical and mental dimensions of quality of life in people aged 75 years and older | killexams.com real questions and Pass4sure dumps

    Introduction

    Quality of life has been defined by the World Health Organization Quality of Life Group as “an individual’s perception of their position in life in the context of the culture and value system in which they live and in relation to their goals, expectations, standards and concerns” (World Health Organization Quality of Life Group, 1995, p. 1405). Quality of life in community-dwelling older people predicts the adverse outcomes of institutionalization and premature death, even after controlling for disability and frailty (Bilotta et al., 2011). To support independent living in older people, both health care and social care professionals may need to carry out preventive interventions focused on aspects related to quality of life, with the aim of delaying institutionalization and avoiding premature death. Determining the influence of disability on quality of life in older people is important to developing early detection of problems and conducting preventive interventions.

    In addition to lower quality of life, disability is a relevant health outcome for older persons. There are several ways of defining disability. The most widely used is: experiencing difficulty in carrying out activities that are essential to independent living - difficulties in performing activities of daily living (ADL), and/or instrumental activities of daily living (IADL) (Tas et al., 2007a; Tas et al., 2007b). ADL functions are essential for an individual’s self- care (e.g., wash and dry your whole body and get on and off the toilet), whereas IADL functions are more concerned with self-reliant functioning in a given environment (e.g., prepare dinner and do the shopping). ADL disability represents a more severe and later form of disability than IADL disability (Hardy et al., 2005; Wong et al., 2010), resulting in a lower proportion of persons with ADL disability than IADL disability (Akosile et al., 2018; Chatterji et al., 2015).

    Disability is associated with increased health care utilization and related costs (Fried et al., 2004), and premature death (Manton, 1988; Mor et al., 1994; Walter et al., 2001). In addition, disability is associated with impaired quality of life in older people (Akosile et al., 2018; Den Ouden et al., 2013; Gureje et al., 2006; Soósová, 2016). However, the “disability paradox” (Albrecht & Devlieger, 1999) suggests that persons with severe impairments may nevertheless report high quality of life (Watson, 2002), although this paradox seems to dissolve when contextual factors (i.e., personal and environmental situation) are considered (Fellinghauer et al., 2012). Disability has a dynamic nature, so persons can move in and out of disability, with transitions between states of disability (Hardy et al., 2005; Nikolova et al., 2011; Van Houwelingen et al., 2014). Transitions to greater disability were more common than improvements in disability in people aged ≥85 years (Van Houwelingen et al., 2014). In particular, people with more than one chronic disease, depressive symptoms, and cognitive impairment had the highest risk of deteriorating; however, a small number of very old people are able to improve in their disability status (Van Houwelingen et al., 2014).

    The aim of the present cross-sectional study was to determine the influence of both ADL and IADL disability on quality of life, incorporating a physical and a mental dimension, in people aged 75 years and older. In contrast to previous research (Akosile et al., 2018; Den Ouden et al., 2013; Gureje et al., 2006; Soósová, 2016) the main focus here is on the associations between ADL and IADL items and quality of life. Items are more concrete than the types of disability (ADL, IADL) and thereby provide health care professionals (e.g., nurses, general practitioners, physiotherapists) and professionals working in the social domain (e.g., social workers, domestic help) specific targets to enhance quality of life in older people. To enhance quality of life of older people, it is relevant to know which items of ADL and IADL are associated with lower quality of life because the interventions will be carried out by different professionals. For example, if an older person can no longer wash and undress themselves, a district nurse can provide support (e.g., in the Netherlands) and if an older person has difficulties performing household activities then domestic help can provide the necessary support.

    Methods Study population and data collection

    The Senioren Barometer is a web-based questionnaire used to assess the opinion of a panel of Dutch older people (aged 50 years and older) about different aspects of life. This questionnaire has been used in previous studies (Gobbens, Luijkx & Van Assen, 2013; Gobbens, Van Assen & Schalk, 2014).

    In the period from December 2009 to January 2010 1,492 respondents completed at least part of the questionnaire, of whom 1,031 filled out the section on background characteristics, quality of life, and disability. Because disability is associated with greater age (Tas et al., 2007a; Tas et al., 2007b) the author selected only people aged 75 years and older (n = 377). As described in previous studies using the Senioren Barometer, older people can volunteer, and participation is always without obligation. The sample was invited to participate in the study in different ways and through multiple sources. First, people could indicate through the website (http://www.seniorenbarometer.nl) that they wanted to complete the questionnaire. Second, organizations for older people in the Netherlands were approached and asked to issue an announcement of the study on their websites so that their members who were interested could register. Third, a major source of participants was persons who attended computer training courses for older persons given by a large training and educational institute in the Netherlands.

    Medical ethics approval was not necessary as particular treatments or interventions were not offered or withheld from respondents. The integrity of respondents was not encroached upon as a consequence of participating in the study, which is the main criterion in medical-ethical procedures in the Netherlands (Central Committee on Research inv. Human Subjects, 2010). Informed consent in relation to detailing the study and maintaining confidentiality was observed.

    Measures Quality of life

    The author used the Short-Form Health Survey (SF-12) for measuring quality of life (Ware Jr, Kosinski & Keller, 1996). The SF-12 is a shorter version of the SF-36 (Ware Jr & Sherbourne, 1992) that uses only 12 questions. The SF-12 is developed to replicate the SF-36 with the aim to minimalize respondent burden. The 12 items are used to derive two summary quality of life measures, the physical dimension (six items) and the mental dimension (six items); their scores range from 0 to 100. Higher scores refer to better quality of life. Several studies have reported the validity and reliability of the SF-12 as a measure of quality of life in the general population, including older people (Bentur & King, 2010; Cernin et al., 2010; Jakobsson et al., 2012; Kim et al., 2014; Kontodimopoulos et al., 2007). In the present study, the (unstandardized) Cronbach’s alpha was .82 for the physical dimension and .73 for the mental dimension; an adequate value of the Cronbach’s alpha is between .70 and .90 (Cortina, 1993).

    Disability

    The author used the Groningen Activity Restriction Scale (GARS) for assessing disability (Kempen & Suurmeijer, 1990). The GARS is a self-report questionnaire consisting of two subscales. The first subscale measures ADL (11 items) and the second subscale relates to IADL (seven items). Each item has four response options: (1) able to perform the activity without any difficulty, (2) able to perform the activity with some difficulty, (3) able to perform the activity with great difficulty, and (4) unable to perform the activity independently. A distinction can then be made in two categories, complete independence and dependency (more or less). The disability total score ranges from 18 (no disability) to 72 (maximum disability). Following Ormel et al. (2002) the cut-point of 29 has been chosen for the disabled group because this cut-point corresponds with the 85th percentile of the GARS in a large sample of older people (Kempen et al., 1996b). The scores for the ADL and IADL subscales range from 11 to 44 and 7 to 28, respectively, with higher scores indicating greater disability; cut-points for these subscales do not exist. The GARS has shown good psychometric properties for assessing disability in older people (Kempen et al., 1996a). In this study, the (unstandardized) Cronbach’s alpha’s for ADL and IADL disability were .82 and .80, respectively, representing adequate values (Cortina, 1993)

    Background characteristics: sociodemographic and multimorbidity

    The sociodemographic background characteristics considered were age, sex, marital status, education level, and net household income. See Table 1 for a detailed description of the answer categories. Multimorbidity was assessed by asking the respondents, “Do you have two or more diseases and/or chronic disorders?” (yes/no).

    Analysis strategies

    First, the author determined the characteristics of the sample using descriptive statistics. Second, the quality of life dimensions (physical, mental) scores for non-disabled and disabled participants were compared using student’s t–tests assuming unequal population variances. Effect size was assessed with Cohen’s d, assuming equal population variances; .2, .5, .8 corresponding to small, medium, large effect size, respectively (Cohen, 1988). Correlations of ADL and IADL disability with the physical and the mental dimensions of the SF-12 were also examined. According to Cohen, correlations were considered as small, medium, or large with coefficients of .1, .3, or .5, respectively (Cohen, 1988).

    Table 1:

    Participant characteristics (N = 377).

    Characteristic n(%) Age, mean ± SD, range 79.8 ± 3.7, 75–95 Sex, % of men 261 (69.2) Marital status Married or cohabiting 244 (64.8) Single 36 (9.5) Divorced 11 (2.9) Living apart together 3 (0.8) Widowed 83 (22.0) Education None 30 (8.0) Primary 34 (9.0) Secondary 160 (42.4) Polytechnics and higher vocational training 113 (30.0) University 40 (10.6) Incomea €999 - or less 7 (2.1) €1,000–€1,499 44 (13.2) €1,500-€1,999 54 (16.1) €2,000–€2,499 90 (27.0) €2,500–€2,999,- 54 (16.1) €3,000–€3,499,- 38 (11.4) €3,500–€3,999,- 25 (7.5) €4,000–€4,499,- 11 (3.3) €5,000 or more 11 (3.3) Multimorbidity, % yes 166 (44.0) GARS Total disability 95 (25.2) ADL disability, mean ± SD, range 13.6 ± 3.8,11–33 Dress yourself 55 (14.6) Get in and out of bed 31 (8.2) Stand up from sitting in a chair 53 (14.1) Wash your face and hands 6 (1.6) Wash and dry your whole body 57 (15.1) Get on and off the toilet 12 (3.2) Feed yourself 4 (1.1) Get around in the house (if necessary, with a cane) 18 (4.8) Go up and down the stairs 134 (35.5) Walk outdoors (if necessary, with a cane) 75 (19.9) Take care of your feet and toenails 183 (48.5) IADL disability, mean ± SD, range 11.2 ± 4.5, 7–28 Prepare breakfast or lunch 14 (3.7) Prepare dinner 88 (23.3) Do “light” household activities 69 (18.3) Do “heavy” household activities 212 (56.2) Wash and iron your clothes 169 (44.8) Make the beds 185 (49.1) Do the shopping 86 (22.8) SF-12 Physical dimension of quality of life, mean ± SD, range 66.9 ± 25.6, 0–100 Mental dimension of quality of life, mean ± SD, range 74.5 ± 18.7, 10–100

    Before carrying out regression analyses some sociodemographic variables were coded for analysis. As in a previous study, the author created dummies for sex (“1” woman, “0” man), marital status (“1” married or cohabiting, “0” rest) and multimorbidity (“1” yes, “0” no), and linear effects of age and level of education were incorporated into the analyses (Gobbens, Luijkx & Van Assen, 2013). Bivariate associations between one background variable or disability item on the one hand and one quality of life dimension (physical, mental) on the other hand were tested using regression analyses. Subsequently, the author examined the effects of each variable (background variables, disability items) on the physical and mental dimensions in four multiple linear regression analyses, controlling for all the other variables in the model. The simplest model only assessed the effects of all background variables together. One model also included all 11 ADL disability items, whereas another model also included the seven IADL items together with the background variables. The most complex model included all 24 items. The fit (explained variance) of all four models was tested (R2) and compared (delta R2). Power analyses using GPower 3.1.0 (Faul et al., 2007) showed that the sequential linear regression analyses on 377 participants had a power of at least 80% to detect an effect of Cohen’s f2 = .056 which is a small to medium effect size (Cohen, 1988).

    Data were processed using SPSS version 24.0 (IBM Corporation, Armonk, NY, USA). All reported p-values are two-tailed. A p-value <0.05 was considered statistically significant.

    Results Participant characteristics

    See Table 1 for an overview of the descriptive statistics of the participant characteristics. The mean age of the participants was 79.8 (SD = 3.7), 69.2% were male, and 64.8% were married or cohabiting. The average scores on quality of life for the physical and mental dimensions were 66.9 (SD 25.6) and 74.5 (SD 18.7), respectively. Using the cut-point of 29 on the GARS, 25.2% of the participants were totally disabled, including both the ADL and the IADL subscale. In addition, 54.6% and 67.4% of the participants had at least one ADL disability and IADL disability, respectively. Of the 11 ADL disability items, participants experienced the greatest dependency in relation to taking care of their feet and toenails (48.5%). Of the 7 IADL disability items, participants experienced the greatest dependency in relation to doing “heavy” household activities (56.2%). In general, it should be noted that the percentages of the IADL disability items are higher than the percentages of the ADL disability items; five IADL disability items scored higher than 20% versus two ADL disability items (see Table 1).

    Differences between non-disabled and disabled participants on quality of life

    Table 2 presents the results of comparing disabled and non-disabled people on the physical and the mental dimensions of the SF-12. Disabled participants scored lower on both quality of life dimensions (p-values < 0.001), with very large effect sizes, d = 1.30 for the mental dimension and d = 1.82 for the physical dimension.

    Table 2:

    Comparison of quality of life dimensions between disabled and non-disabled participants.

    Non-disabled n = 279 M (SD) Disabled n = 95 M (SD) Results t-testa Effect size Cohen’s db Physical dimension of quality of life 76.19 (19.29) 39.61 (22.27) t(144.95) = 14.29 < 0.001 d = 1.82 Mental dimension of quality of life 79.85 (14.59) 58.68 (20.46) t(128.05) = 9.31 < 0.001 d = 1.30 Correlations between disability and quality of life

    Table 3 shows the correlations between ADL disability, IADL disability, physical quality of life, and mental quality of life. Most correlations were strong (>5); only the correlation between ADL disability and mental quality of life could be considered as medium (.483) (all p-values < 0.001).

    Table 3:

    Correlations between ADL disability, IADL disability, physical and mental dimensions of quality of life.

    IADL disability Physical quality of life Mental quality of life ADL disability 0.702 −0.683 −0.483 IADL disability −0.676 −0.541 Physical quality of life 0.734 Regression analyses: effects of ADL and IADL disability items on quality of life

    Table 4 presents the results of the bivariate and sequential linear regression analyses on the physical and mental quality of life dimensions of the SF-12. The table shows the effects of six background characteristics, 11 ADL disability items, and seven IADL items on the two dimensions of quality of life (physical, mental). Columns 2–4 and 8–10 present the bivariate regressions. Being a man, younger age, married or cohabiting, higher education, higher income, and no multimorbidity were associated with higher scores on both the physical and mental dimensions. Of the 11 ADL disability items, all were associated with physical quality of life and 10 were associated with mental quality of life. The exception was the item “feed yourself” (p = 0.058). All seven IADL disability items were associated with both quality of life dimensions.

    Table 4:

    Effect of background characteristics, ADL and IADL disability items on the physical and mental dimensions of quality of life.

    Physical dimension of quality of life Mental dimension of quality of life Bivariate Multiple Bivariate Multiple B SE p B SE p B SE p B SE p Background characteristics Sex (women) −11.92 2.81 <0.001 −2.81 2.15 0.192 −4.89 2.08 0.019 2.26 2.10 0.283 Age −0.73 0.36 0.040 0.27 0.23 0.251 −0.81 0.26 0.002 −0.06 0.23 0.801 Marital status (married) 8.48 2.74 0.002 −0.83 2.08 0.692 5.18 2.01 0.010 0.99 2.04 0.626 Education 4.86 1.26 <0.001 0.62 0.88 0.481 2.86 0.92 0.002 0.22 0.86 0.795 Income 3.60 0.74 <0.001 0.33 0.52 0.524 1.92 0.52 <0.001 0.35 0.51 0.496 Multimorbidity −29.13 2.21 <0.001 −13.35 1.82 <0.001 −13.50 1.82 <0.001 −4.03 1.78 0.024 ΔR2 0.364 <0.001 0.162 <0.001 ADL disability items Dress yourself −27.20 2.45 <0.001 −6.98 3.09 0.024 −12.63 1.96 <0.001 −0.12 3.02 0.967 Get in and out of bed −32.04 3.72 <0.001 −6.95 3.42 0.043 −17.72 2.82 <0.001 −6.57 3.34 0.050 Stand up from sitting in a chair 26.94 2.90 <0.001 −5.68 2.59 0.029 −16.63 2.19 <0.001 −5.55 2.53 0.029 Wash your face and hands −27.89 5.92 <0.001 2.07 5.50 0.707 −12.96 4.39 0.003 2.70 5.37 0.615 Wash and dry your whole body −24.35 2.19 <0.001 2.01 3.10 0.516 −12.28 1.73 <0.001 0.95 3.03 0.754 Get on and off the toilet −37.37 7.27 <0.001 0.99 4.94 0.841 −25.07 5.33 <0.001 −4.28 4.82 0.375 Feed yourself −40.77 12.72 0.001 −11.28 9.22 0.222 −17.79 9.36 0.058 −3.12 9.00 0.729 Get around in the house (if necessary, with a cane) −24.78 4.24 <0.001 6.43 3.37 0.057 −13.93 3.15 <0.001 1.33 3.29 0.686 Go up and down the stairs −21.00 1.23 <0.001 −5.78 1.63 <0.001 −10.62 1.07 <0.001 −0.84 1.59 0.597 Walk outdoors (if necessary, with a cane) −20.33 1.60 <0.001 0.03 1.82 0.985 −11.03 1.28 <0.001 0.93 1.77 0.601 Take care of your feet and toenails −10.80 0.88 <0.001 1.41 0.86 0.101 −5.29 0.71 <0.001 1.35 0.83 0.108 ΔR2 0.058 <0.001 0.045 0.012 IADL disability items Prepare breakfast or lunch −14.34 4.11 0.001 6.83 3.34 0.042 −7.97 3.01 0.009 2.07 3.26 0.526 Prepare dinner −5.89 1.42 <0.001 0.74 1.15 0.517 −3.29 1.05 0.002 0.003 1.12 0.998 Do “light” household activities −19.29 1.74 <0.001 −1.52 1.68 0.368 −9.83 1.37 <0.001 0.83 1.64 0.613 Do “heavy” household activities −14.27 0.75 <0.001 −6.57 1.05 <0.001 −8.64 0.62 <0.001 −4.55 1.03 <0.001 Wash and iron your clothes −7.49 1.05 <0.001 −1.03 0.91 0.260 −4.03 0.79 <0.001 0.58 0.89 0.516 Make the beds −13.05 0.88 <0.001 −1.23 1.07 0.254 −7.90 0.70 <0.001 −1.83 1.05 0.083 Do the shopping −18.06 1.37 <0.001 −5.41 1.42 <0.001 −11.13 1.06 <0.001 −5.74 1.39 <0.001 ΔR2 0.108 <0.001 0.135 <0.001 ΔR2 ADL and IADL 0.350 <0.001 0.282 <0.001 R2 total 0.714 <0.001 0.444 <0.001

    Columns 5–7 and 11–13 summarize the results of the sequential linear regression analyses. R2 total indicates that 71.4% and 44.4% of the physical and mental quality of life dimensions were explained by all the predictors together, respectively. After controlling for the background variables (sociodemographic characteristics, multimorbidity), disability (ADL and IADL items together) explained 35.0% of physical quality of life and 28.2% of mental quality of life, with both p-values <0.001. The ADL disability items together explained 5.8% and 4.5% of the physical and mental dimension, with p-values <0.001 and 0.012, respectively, after controlling for all background characteristics and IADL disability items, representing a medium to large effect size (f2 = .20) and a small to medium effect size (f2 = .08), respectively. The IADL disability items together explained a significant part of both quality of life dimensions after controlling for background characteristics and ADL items, with increases in explained variance of 10.8% (physical; f2 = .38, large effect size) and 13.5% (mental; f2 = .24, medium to large effect size) (both p-values <  0.001).

    In addition, Table 4 presents the effects of each of the background characteristics and individual ADL and IADL items on physical and mental quality of life. The columns five and 11 show the regression coefficients with corresponding standard errors (columns six and 12) and p-values (columns seven and 13).

    Before interpreting the effects of individual items after controlling for the other variables, the author checked for multicollinearity. As the variance inflation factors (VIF) for all items were smaller than 5, which is below the threshold of 10 (Yu, Jiang & Land, 2015), the author relied on his estimates as they are not strongly affected by multicollinearity.

    Of the background variables, only multimorbidity was negatively associated with quality of life, both physical and mental. None of the other background characteristics were associated with quality of life, after controlling for all the other variables in the model.

    Of the 11 ADL disability items only four were significantly associated with quality of life. The ADL item “stand up from sitting in a chair” was negatively associated with both dimensions (physical, mental). The ADL items “dress yourself”, “get in and out of bed”, and “go up and down the stairs” were only negatively associated with the physical dimension of quality of life. Of the seven IADL disability items, three were associated with quality of life. The two IADL items (do “heavy” household activities, do the shopping) were negatively associated with both the physical and mental dimensions of quality of life and “prepare breakfast or lunch” was positively associated with the physical dimension. All effect sizes (f2) of the individual ADL and IADL disability items on physical as well as mental quality of life were <.15, representing small effect sizes. Of all ADL disability items, “go up and down the stairs” and “stand up from sitting in a chair” had the largest effect sizes on the physical and mental quality of life dimensions, f2 = .042 and f2 = .016, respectively. Of all IADL disability items, the item with the largest effect sizes on the physical as well as the mental dimension of quality of life was “do “heavy” household activities”, with f2 = .13 and .065, respectively.

    Discussion

    In this study the author determined the associations between ADL and IADL disability items and quality of life in a sample consisting of 377 Dutch people aged 75 years or older. The author used two validated questionnaires, the GARS for assessing disability and the SF-12 for assessing quality of life, containing a physical and a mental dimension. To the best of my knowledge, the present study was the first using the GARS and the SF-12 to determine the associations between disability and quality of life. In addition, no previous study paid attention to the predictive value of the individual ADL and IADL disability items on quality of life.

    The bivariate regression analyses showed that the following factors were associated with physical quality of life as well as mental quality of life: being a man, younger age, married or cohabiting, higher education, higher income, no multimorbidity, ten ADL disability items, and seven IADL disability items. The ADL disability item “feed yourself” was not associated with the mental dimension. However, the sequential linear regression analyses revealed that only multimorbidity, ADL item “stand up from sitting in a chair”, and IADL items “do ‘heavy’ household activities” and “do the shopping” were significantly associated with both quality of life dimensions, after controlling for all the variables in the model.

    The finding that multimorbidity is associated with lower quality of life in older people is supported by previous studies in several countries using different measurement instruments (Brettschneider et al., 2013; Fortin et al., 2006; Garin et al., 2014; Gu et al., 2018). In Germany, quality of life of multimorbid people aged 65 to 85, assessed with the EQ-5D and the EQ-5D visual analogue scale (EQ-VAS) (Rabin & De Charro, 2001), decreased with an increasing count and severity of chronic conditions (Brettschneider et al., 2013). In Canada, 238 people completed the SF-36 (Ware Jr & Sherbourne, 1992) for assessing quality of life, and multimorbidity was measured by counting the number of chronic diseases and with the Cumulative Illness Rating Scale (CIRS) (Linn, Linn & Gurel, 1968); this study showed that the physical health dimension of quality of life deteriorated more than the mental health dimension of quality of life with increasing multimorbidity (Fortin et al., 2006). A study among Spanish people (≥50 years) also demonstrated that the number of chronic diseases was associated with lower quality of life (Garin et al., 2014), assessed with the WHOQOL-AGE (Caballero et al., 2013). Finally, a longitudinal study conducted in China showed that distinct multimorbidity patterns had various impacts on different dimensions of quality of life among community-dwelling older people (Gu et al., 2018). These findings are important because multimorbidity is frequently present in older people; in the age group 75–84 years the prevalence is 71.7% (Abad-Diez et al., 2014). The author recommends more studies focusing on the impact of multimorbidity patterns on quality of life in other countries. These studies should focus in particular on effects of combinations of common chronic diseases on quality of life, thereby providing direction to (preventive) interventions.

    All ADL disability items combined explained a significant part of the variance of both the physical dimension and the mental dimension of quality of life. Another study showed that maintaining independence in ADL had a positive effect on four domains of the WHOQOL-OLD (sensory abilities; autonomy; past, present, and future activities; social participation) (Power, Quinn & Schmidt, 2005), and one domain of the WHOQOL-BREF (physical health) (The WHOQOL Group, 1998; Soósová, 2016)). Quality of life, assessed with the WHOQOL-OLD (Power, Quinn & Schmidt, 2005) and the WHOQOL-BREF (The WHOQOL Group, 1998), were significantly associated with ADL disability in two samples of Nigerian older people aged 65 years and older (Akosile et al., 2018; Gureje et al., 2006). A Dutch study including a total of 537 middle-aged and older persons also found that quality of life, assessed with the SF-36 (Ware Jr & Sherbourne, 1992), was associated with ADL disability, measured with the Katz-questionnaire (Katz & Akpom, 1976; Den Ouden et al., 2013). In particular, health care professionals (e.g., district nurses, physiotherapists, general practitioners, occupational therapists) should identify (potential) limitations in performing ADL at an early stage in order to maintain or increase quality of life in older people. Based on the present study, special attention is needed to address problems people have when standing from sitting, because this activity is associated with lower physical and mental quality of life.

    All IADL disability items combined explained a larger part of the variance of both the physical and the mental dimension of quality of life compared with all ADL disability items together, 10.8% versus 5.8% and 13.5% versus 4.5%, respectively. Two studies referred to above also found that IADL disability was associated with quality of life (Akosile et al., 2018; Gureje et al., 2006). The finding that IADL disability items were more prevalent than ADL disability is supported by other studies (Akosile et al., 2018; Bleijenberg et al., 2017; Hu et al., 2012) and contributes to the evidence that IADL disability occurs earlier than ADL disability; probably because IADL is more complex and appeals more to cognitive function. In Nigeria the prevalence figure of IADL disability was 39.3% versus ADL disability 32.5% (Akosile et al., 2018). Among Dutch older people, with an average age of 74.6 years, carrying out household tasks was the most frequent problem (44.8%), followed by travelling (26.9%), and grocery shopping (23.0%) (Bleijenberg et al., 2017). In particular, the first and the last item are important because the present study showed that these two items were associated with the physical as well as the mental dimension of quality of life in older people. These findings have not been available to date. Conducting interventions on problems that older people can experience with performing heavy household activities and shopping could help them reach a higher quality of life. Domestic help may meet these needs or additionally reablement or restorative care services may be of benefit. These are short term services aimed at improving the independence of older people so they can hopefully go back to living independently without ongoing assistance.

    The model including all the prediction variables explained a large part of the variance in scores of the physical and mental dimensions of the SF-12, 71.4% and 44.4%, respectively. In a sample of community-dwelling older Dutch people (n = 8,928) it was shown that people experiencing disability, multimorbidity, and frailty scored lower on quality of life compared with people experiencing individual conditions (Lutomski et al., 2014). It is possible that the explained variances in the scores of the quality of life dimensions were also greater if depression as a predictive variable was included in the model; a review, including 74 studies, found an association between depression and lower quality of life in older people, independent of how quality of life was assessed (Sivertsen et al., 2015).

    This study has some limitations. First, the cross-sectional nature of this study does not allow strict cause–effect interpretations of the associations between the ADL and IADL disability items and quality of life. A longitudinal study is recommended to establish such associations. Second, disability was assessed by the GARS, a self-report measure, that does not include performance-based measures. A combination of both measures may be the best way to fully capture the picture of disability in ADL and IADL. However, in a sample of oldest old (≥80 years) it was demonstrated that self-assessments for disability in ADL and IADL reliably reflect direct assessment in performance (Bravell, Zarit & Johansson, 2011). Third, the author used the Senioren Barometer for data collection. This is a web-based questionnaire, so access to Internet was necessary for participating in the present study; this may have led to selection bias. In this context, it should be noted that in the study sample 69.2% were men, while in the Dutch population aged 75 years and older, only 37.9% are men, as established January 1, 2010 (Statistics Netherlands, 2017).

    Conclusions

    In this study the author showed that disability in ADL and IADL is negatively associated with quality of life in older people. Therefore, it is important for health care professionals to carry out interventions aimed at preventing and diminishing disability or its adverse outcomes, such as a lower quality of life. Promising interventions are multidisciplinary and multifactorial in nature, should be preceded by an individualized assessment, and should involve case management and long-term follow up (Daniëls et al., 2010). Lifestyle interventions targeting physical exercise, nutrition, and cognition appear to be effective against disability in ADL and IADL; in order to be actually effective, these interventions should be inexpensive, feasible, and easy to implement (Fougère et al., 2018). In line with the findings of the present study, it is recommended to first focus on the disability items that have the greatest impact on quality of life of older people (“stand up from sitting in a chair”, “do ‘heavy’ household activities” and “do the shopping”) to achieve the best outcome.

    Supplemental Information Raw data exported from the Seniorenbarometer 2009 applied for data analysis and preparation for Tables 1– 4

    The plant cell wall integrity maintenance and immune signaling systems cooperate to control stress responses in Arabidopsis thaliana | killexams.com real questions and Pass4sure dumps

    INTRODUCTION

    Plants adapt to diverse environments by modifying their architecture. The cell walls surrounding all plant cells are key elements enabling this adaptability and consist of different components including proteins and polysaccharides such as cellulose, hemicelluloses, pectins, and lignin (1). These components are synthesized in different subcellular compartments and have specialized functions. Cellulose is synthesized by plasma membrane–localized cellulose synthase complexes and released into the adjacent extracellular space as strands that form microfibrils before being incorporated into the wall where they function as the main load-bearing element. The walls are also essential elements underlying growth, development, and resistance to biotic and abiotic stresses, all of which influence crop yield (2, 3). This is illustrated by mutations that improve yields of staple crops like maize and rice by affecting cell wall biosynthesis, homeostasis, polysaccharide modifications, and signaling components (4, 5).

    Cell wall plasticity describes the ability of plant cell walls to adapt to dynamic and challenging growth conditions. Plasticity and the resulting recalcitrance in cell wall biochemistry and structure against targeted manipulation also represent a major challenge to producing energy from lignocellulosic biomass (6). The available evidence suggests that the plant cell wall integrity (CWI) maintenance system forms an integral element of cell wall plasticity (7–9). This mechanism seems to involve receptor-like kinases (RLKs) and ion channels that constantly monitor the state of the cell wall and initiate adaptive changes in both cellular and cell wall metabolism in response to cell wall damage (CWD) (10–12). Here, we refer to any changes to cell wall structure or composition that impair CWI as CWD. Because CWD may be induced by various means, both ligand-mediated mechanisms and mechanoperception could be involved in CWD detection. For example, pathogen-derived enzymes break down cell walls, which releases cell wall–derived fragments. This could lead to cell wall weakening, deformation, and displacement of the cell wall relative to the plasma membrane and can eventually result in cell bursting due to the high turgor pressure of the cell (13, 14). The cell wall fragments, such as cellobiose or oligogalacturonides (OGs; fragments of pectic polysaccharides), can activate plant immune responses (15, 16). Although OGs are detected through wall-associated receptor kinases (WAKs), the receptors for cellobiose have not been identified (7). Mechanosensitive systems may also be activated by CWD that compromises the structural integrity of the cell wall (17). In addition to the enzymatic actions of pathogens and mechanical damage caused by breakage or grazing, defects in cell wall biosynthetic processes can also cause CWD by preventing the production of load-bearing structural elements (18).

    Although several candidate genes have been implicated in CWD perception, experimental evidence confirming their involvement is scarce (7, 10, 19). Among the candidates identified in Arabidopsis thaliana are two homologs of plasma membrane–localized RLK1-like proteins originally in Catharanthus roseus (CrRLK1Ls): THESEUS1 (THE1) and FERONIA (FER) (20, 21). Additionally, the leucine-rich repeat (LRR) RLK MALE DISCOVERER 1 (MDIS1)–INTERACTING RECEPTOR-LIKE KINASE 2 (MIK2), WALL-ASSOCIATED KINASE 1 (WAK1) and WAK2, as well as the putatively stretch-activated, mechanosensitive Ca2+ channel MATING PHEROMONE INDUCED DEATH 1 (MID1)–COMPLEMENTING ACTIVITY 1 (MCA1) have also been implicated in CWI maintenance in Arabidopsis (22–26). MCA1 was originally identified through its ability to partially complement a Saccharomyces cerevisiae strain deficient for MID1-CCH1 (calcium channel homolog 1), which is required for CWI maintenance in this yeast (22, 27). Homologs of MCA1 and THE1 have been identified in Oryza sativa (OsMCA1), Zea mays (NOD), and Marchantia polymorpha (MpTHE), suggesting that these proteins may participate in cell wall maintenance across the plant kingdom (28–30). A characteristic feature of THE1, FER, WAK1, and WAK2 is the presence of domains that may bind cell wall–derived epitopes or ligands (8, 10, 31). However, binding to cell wall components has been confirmed only for WAK1, WAK2, and FER (8, 21, 32). THE1, FER, and MIK2 are required for resistance to the fungal pathogen Fusarium oxysporum f. sp. conglutinans, implicating CWI signaling also in biotic stress responses (25, 33).

    CWD induced by the inhibition of cellulose production stimulates the compensatory production of the cell wall components callose and lignin; accumulation of the hormones jasmonic acid (JA), salicylic acid (SA), and ethylene; generation of reactive oxygen species (ROS); and activation of Ca2+-based signaling, implicating all of these processes in CWI maintenance (11, 23, 34–37). CWD in A. thaliana promotes the generation of ROS in the apoplast—the space adjacent to the plasma membrane that contains the cell wall—by RESPIRATORY BURST OXIDASE HOMOLOG D (RBOHD) (23). The activity of RBOHD is regulated by both Ca2+-dependent and Ca2+-independent mechanisms, with the latter requiring BOTRYTIS-INDUCED KINASE 1 (BIK1), which is a substrate of BRASSINOSTEROID INSENSITIVE 1 (BRI1)–ASSOCIATED KINASE 1 (BAK1) and other RLKs during pattern-triggered immunity (PTI) (38, 39). BAK1 acts as a co-receptor for LRR-RLKs and, as such, plays an important role in responses to pathogen-associated molecular patterns (PAMPs) such as flagellin, the flagellin-derived epitope flg22, elongation factor–thermo unstable (EF-Tu), and the EF-Tu–derived epitope elf18, as well as damage-associated molecular patterns (DAMPs) such as the AtPep1 and AtPep3 peptides (40–42). AtPep1 and AtPep3 precursor peptides are encoded by the PROPEP1 and PROPEP3 genes, which are induced by pathogen infection and wounding (43). The precursor peptides are reportedly released into the apoplastic space where they are likely processed to give rise to the active form (43). Application of AtPep peptides enhances both expression of their own PROPEP genes, creating a positive feedback loop, and PTI-controlled defense responses. PTI and CWI maintenance may complement each other during plant defense, but such regulatory interactions between CWI and PTI signaling have not been characterized (44–46).

    Here, we investigated the responses to different types of CWD to understand the cellular events underlying CWD perception. We analyzed CWD responses in 27 A. thaliana genotypes to establish the functions of candidate genes in CWI maintenance and performed genetic analyses to assess whether key CWI signaling elements belong to one or more signaling cascades. We found that CWD induced the expression of AtPROPEP1 and AtPROPEP3 as well as the release of a PROPEP3 fusion protein. In contrast, application of AtPep1 and AtPep3 repressed CWD-induced phytohormone production, thus identifying a mechanism through which PTI signaling and the CWI maintenance mechanism cooperate to regulate defense responses.

    RESULTS CWD responses induced by different stimuli are osmosensitive

    We used an Arabidopsis seedling–based model system to investigate how plants respond to different types of CWD and elucidate further the role of turgor pressure in CWD perception (11). CWD was induced using either Driselase, a mix of several cell wall–degrading enzymes from Basidiomycetes sp., or isoxaben (ISX), a herbicide that blocks cellulose biosynthesis (13, 18). We chose Driselase because this enzyme mix is similar to the enzyme cocktail released by fungal pathogens during infection (47–49). Furthermore, the enzymes lead to cell wall fragmentation, thus directly causing CWD regardless of cell type, differentiation stage, or turgor pressure. We chose ISX because it inhibits cellulose production only in actively elongating cells (for example, in the root elongation zone). It causes CWD in conjunction with the naturally high turgor pressure of plant cells because it reduces the number of load-bearing cellulose microfibrills in the walls, thus making the cell wall susceptible to failure. This is illustrated by the suppression of ISX-induced lignin, callose, JA, and SA accumulation; tissue lesion formation; and redistribution of carbohydrates by addition of osmotica like sorbitol or mannitol (11, 50). Similar effects have also been reported in yeast cells exposed to CWD, indicating the importance of turgor pressure in CWI maintenance (51).

    First, we investigated the effect of ISX or Driselase treatment on the morphology of Wave 131Y seedlings, which ubiquitously express membrane-localized yellow fluorescent protein (YFP), in the presence or absence of an osmoticum (sorbitol) in time course experiments (52). Whole seedlings were grown submerged in liquid culture (11), and the medium was exchanged for fresh medium containing the ISX solvent dimethyl sulfoxide (DMSO), sorbitol, Driselase, ISX, Driselase with sorbitol, or ISX with sorbitol at the 0-hour time point. At 7 hours, epidermal cells in the root elongation zone of ISX-treated seedlings exhibited a swollen phenotype, which was reduced by cotreatment with sorbitol (fig. S1A). Driselase treatment resulted in degradation of the root tip (including the elongation zone) after 7 hours, leaving behind only larger, already fully elongated cells for visualization (fig. S1B). This degradation was possibly enhanced by the addition of osmoticum. Because the effects on roots were so pronounced after 7 hours of treatment, we did not investigate the phenotypic effects in roots at later time points. In cotyledons of ISX-treated seedlings, the plasma membrane marker signal was similar to that in cotyledons from DMSO controls after 7 hours but was lost in patches after 24 hours (fig. S2, A and B). Sorbitol treatment alone had no effect on membrane marker intensity but, when coadministered with ISX, restored the marker signal at the 24-hour time point. Driselase treatment resulted in the formation of patches lacking the membrane marker after 7 hours (fig. S2C). These patches seemed to be more pronounced after 24 hours and were not affected by the addition of sorbitol (fig. S2, C and D).

    After establishing the dynamics of CWD responses, we used the same experimental setup to investigate cell death and the deposition of lignin and callose in cotyledons and the accumulation of JA and SA in whole seedlings (11). We also included isoxaben resistant1-1 (ixr1-1) and bak1-5 mutant seedlings in the studies. The ixr1-1 mutation causes an amino acid substitution in CELLULOSE SYNTHASE A3 (CESA3) that renders the protein resistant to inhibition by ISX (53), thus providing a control for ISX specificity. Plants carrying the bak1-5 allele are only impaired in immune responses triggered by LRR-RLKs but not in brassinosteroid-dependent signaling, making the plants a suitable control for detecting the involvement of either DAMPs (for example, AtPep1) generated by CWD or PAMPs that are possibly present as contaminants in Driselase (54) and perceived by LRR-RLKs. Col-0 (wild type) and bak1-5 seedlings that were mock (DMSO)–treated or treated with boiled (inactivated) Driselase exhibited no cell death in cotyledons (fig. S3, A and B). ixr1-1 seedlings treated in the same manner exhibited a slight increase in cell death compared to Col-0. Treatment with ISX induced cell death in Col-0 and bak1-5, but not in ixr1-1, cotyledons compared to mock-treated controls. Driselase treatment induced cell death in all genotypes examined. Sorbitol addition suppressed ISX-induced cell death but had no effect on Driselase-induced cell death.

    We also analyzed compensatory lignin deposition in the cotyledons of seedlings that were treated with DMSO, ISX, boiled Driselase, or Driselase, with or without sorbitol, for 24 hours (fig. S3C). Lignin deposition was detectable after ISX treatment in vascular tissue areas in Col-0 and bak1-5, but not in ixr1-1, seedlings. Driselase-treated Col-0, bak1-5, and ixr1-1 seedlings exhibited more ubiquitous lignin deposition. The addition of sorbitol reduced lignin deposition in all cases examined. bak1-5 and ixr1-1 cotyledons seemed more sensitive to Driselase treatment than Col-0 cotyledons based on cell death and lignin deposition phenotypes (fig. S3, A and C).

    Next, we investigated compensatory callose deposition in the cotyledons of seedlings treated in the same manner. Whereas there was no detectable callose deposition in mock- or boiled Driselase–treated Col-0 and bak1-5 cotyledons, callose was deposited in ixr1-1 cotyledons subjected to these same control treatments (fig. S3D). Sorbitol alone had no effect on callose deposition in Col-0 and bak1-5 cotyledons but reduced callose deposition in both ixr1-1 treatment groups. ISX treatment induced callose deposition strongly in Col-0 seedlings, moderately in bak1-5 seedlings, but not in ixr1-1 seedlings. Sorbitol cotreatment with ISX reduced callose deposition in Col-0 and bak1-5. Driselase treatment induced callose deposition in Col-0, but not in bak1-5, cotyledons and did not induce more callose deposition in ixr1-1 seedlings than did boiled Driselase treatment (fig. S3D). bak1-5 plants also exhibit reduced flg22-induced callose deposition, suggesting that the lack of induction observed here is part of a more general defect (55). The lack of significant increase in callose deposition in ixr1-1 was possibly caused by the combination of the substantial amount of basal callose deposition in mock conditions and limited callose induction by Driselase.

    We next quantified phytohormones in Col-0, bak1-5, and ixr1-1 seedlings treated in the same manner as before. JA and SA abundances were low in mock-treated Col-0 and bak1-5 seedlings and slightly increased in ixr1-1 seedlings (Fig. 1, A and B). ISX treatment induced JA accumulation in bak1-5 seedlings more than in Col-0 seedlings, but no induction was observed in ixr1-1 seedlings (Fig. 1A). ISX induced SA accumulation in both Col-0 and bak1-5 seedlings to a similar degree, but not in ixr1-1 seedlings (Fig. 1B). Cotreatment with sorbitol repressed ISX-induced JA and SA accumulation in Col-0 and bak1-5. Phytohormone amounts were lower in ixr1-1 seedlings treated with sorbitol or a combination of ISX and sorbitol than in mock-treated ixr1-1 seedlings, suggesting that sorbitol reduced stress in these plants (Fig. 1, A and B). Driselase treatments induced JA and SA accumulation in Col-0, bak1-5, and ixr1-1 seedlings to different degrees compared to treatment with boiled Driselase (Fig. 1, C and D). In contrast to ISX treatments, Driselase treatment reduced SA accumulation in bak1-5 compared to Col-0 (Fig. 1D). Although induction of both JA and SA by Driselase was less pronounced than induction by ISX in Col-0 and bak1-5 (Fig. 1, A to D), sorbitol cotreatments nevertheless reduced or prevented accumulation of JA and SA in all genotypes examined. The results of these experiments suggest that responses to CWD are not restricted to a particular cell type (exemplified by lignin deposition in both vascular and epidermal tissues). Despite apparent differences in damage caused by ISX and Driselase, Col-0 seedlings exhibited similar osmosensitive responses to both types of CWD with respect to callose, lignin deposition, as well as JA and SA accumulation. These observations suggest that both mechanoperception and osmoperception may be required for induction of CWD responses. bak1-5 seedlings, which have defects in PTI, exhibited distinct differences in their responses to the two CWD-inducing stimuli, suggesting some cross-regulation between PTI and CWI signaling.

    Fig. 1 Different types of CWD induce similar osmosensitive responses.

    Quantification of (A) JA and (B) SA, expressed as microgram per gram dry weight (gDW), in Col-0, bak1-5, and ixr1-1 seedlings that had been treated with DMSO (mock), DMSO and sorbitol (S), ISX, or ISX and sorbitol (ISX + S). (C) JA and (D) SA quantification in Col-0, bak1-5, and ixr1-1 seedlings treated with boiled (inactive) Driselase (bDri), bDri and sorbitol (bDri + S), Driselase (Dri), or Driselase and sorbitol (Dri + S). (E) Relative expression of PDF1.2 as determined by quantitative reverse transcription polymerase chain reaction (qRT-PCR) in Col-0 and bak1-5 seedlings treated with bDri or Dri compared to untreated (NT) seedlings. (F) Relative expression of TCH4 in Col-0 seedlings treated as indicated. (G) JA and (H) SA quantification in Col-0 seedlings treated with boiled pectinase (bP), boiled pectinase and sorbitol (bP + S), pectinase (P), or pectinase and sorbitol (P + S). (I) JA and (J) SA quantification in Col-0 seedlings treated with the indicated combinations of boiled (b) or active preparations of cellulase (C), pectinase (P), xylanase (X), and sorbitol (S). All values represent means with error bars indicating SD. n = 4 (A to D) and n = 3 (E to J). Letters a to d (A to J) indicate statistically significant differences according to one-way analysis of variance (ANOVA) and Tukey’s HSD (honestly significantly different) test (α = 0.05) between treatments for each genotype. Asterisks (A to D) indicate statistically significant differences to the wild type (Student’s t test, **P < 0.01; ***P < 0.001; ns, not significant).

    Osmosensitivity distinguishes CWI signaling from DAMP- and PAMP-dependent responses

    To investigate the regulatory processes responsible for the observed CWD-induced phenotypes, we first performed expression analysis of the defense marker PLANT DEFENSIN1.2 (PDF1.2) because it encodes a defense peptide that is involved in both CWD- and PTI-mediated processes (37, 56). In both Col-0 and bak1-5 seedlings, treatment with boiled or active Driselase induced PDF1.2 expression; however, PDF1.2 was less highly expressed in boiled Driselase–treated bak1-5 seedlings compared to boiled Driselase–treated Col-0, and it was more highly induced by Driselase in bak1-5 than in Col-0 seedlings (Fig. 1E). This supports the hypothesis that BAK1 might contribute to the PTI-mediated recognition of factors in the enzyme preparation, which are not removed by boiling, but represses the response to CWD elicited by the active enzymes. To investigate the role of mechanoperception in the response to CWD, we analyzed the expression of a marker for mechanical stimulation [TOUCH4 (TCH4)] in ISX- and Driselase-treated Col-0 seedlings (57). ISX and active Driselase induced TCH4 expression, whereas sorbitol cotreatments reduced it, providing support for an involvement of mechanoperception in the detection of CWD (Fig. 1F).

    The results from the phytohormone measurements in bak1-5 seedlings treated with ISX and Driselase with or without sorbitol, in conjunction with the results from the PDF1.2 expression analysis, suggested that DAMP or PAMP signaling, or both, might also be sensitive to turgor changes. Therefore, we investigated a possible involvement of OG-induced signaling in turgor-sensitive CWI maintenance using gene expression analysis and phytohormone measurements (58). We examined the expression of RETICULINE OXIDASE (RET-OX) and CYTOCHROME P450, FAMILY 81, SUBFAMILY F, POLYPEPTIDE 2 (CYP81F2), both of which are induced by OG and the flagellin derivative flg22 (58), in Col-0 seedlings that had been treated with two different concentrations of OGs in the absence or presence of the osmoticum sorbitol. Expression of both genes was induced by OG treatments but was not sensitive to sorbitol cotreatment (fig. S4, A and B). We also quantified JA and SA in seedlings treated in the same manner. Both OG and sorbitol treatments resulted in only minor changes in phytohormone amounts (fig. S4, C and D). These results showed that OGs can be perceived by the seedlings in our assay and that OG-induced responses, unlike ISX- and Driselase-induced responses, are not osmosensitive, suggesting that CWD responses analyzed here do not involve OG-dependent signaling.

    Next, we quantified RET-OX and CYP81F2 expression and the amounts of JA and SA in seedlings treated with flg22, sorbitol, or both. RET-OX and CYP81F2 expression was not significantly increased by sorbitol treatment alone, increased moderately by flg22 treatment, and increased greatly in seedlings treated with flg22 plus sorbitol (fig. S4, E and F). Treatments with sorbitol and flg22 resulted in changes in JA amounts that were at the lower limit of detection, although both sorbitol alone and flg22 plus sorbitol promoted JA accumulation (fig. S4G). Flg22 induced SA accumulation after 3 and 7 hours of treatment, and cotreatment with sorbitol enhanced SA accumulation after 7 hours (fig. S4H). These results showed that flg22-induced gene expression and JA and SA accumulation are turgor-sensitive. However, sorbitol treatments seem to enhance the flg22-induced responses, contrary to what we observed with the responses to Driselase and ISX. These results suggest that turgor pressure is relevant for flg22-induced responses but that the underlying regulatory process is distinct from CWI maintenance signaling.

    ISX and cell wall–degrading enzymes induce similar osmosensitive responses

    Driselase is a complex mix of enzymes that degrade several different cell wall polymers (59). To investigate whether the effects observed in Driselase-treated seedlings can be assigned to particular enzymatic activities, we obtained homogeneous preparations of the individual enzymes (xylanase, cellulase, and pectinase) that, according to the manufacturer, account for most of the enzymatic activities in commercially prepared Driselase. Initially, we treated seedlings with increasing concentrations of the individual enzymes and measured phytohormone accumulation to establish optimal experimental conditions (fig. S5, A and B). Xylanase did not induce phytohormone production, whereas cellulase induced only SA accumulation. Pectinase treatment increased the abundance of both SA and JA in seedlings in a concentration-dependent manner. On the basis of these tests, we focused primarily on pectinase and cellulase. JA or SA did not accumulate in seedlings treated with boiled enzymes or sorbitol (Fig. 1, G to J). Pectinase treatment induced accumulation of both JA and SA in an osmosensitive manner (Fig. 1, G and H). Because cellulase treatment alone did not induce JA accumulation, we quantified hormone accumulation after combining cellulase with either xylanase or pectinase. Seedlings treated with cellulase plus xylanase exhibited no increase in JA and a moderate increase in SA accumulation, similar to cellulase alone (Fig. 1, I and J, and fig. S5B). Cellulase plus pectinase elicited JA accumulation, which was higher than in the seedlings treated with the individual enzymes (Fig. 1, G and I, and fig. S5A). SA amounts were lower than in pectinase-treated seedlings but higher than in those treated with cellulase alone (Fig. 1, H and J, and fig. S5B). Sorbitol addition reduced phytohormone accumulation in all enzyme treatments examined. These results suggested that the combination of cellulase and pectinase is mainly responsible for the observed JA and SA accumulation in Driselase-treated seedlings. The differences in the effects of individual enzymes suggest that particular types of CWD may induce distinct phytohormone responses.

    It is conceivable that a factor that is released or secreted from cells upon ISX or Driselase treatment is responsible for activation of the CWD responses observed. To test this hypothesis, we measured JA and SA accumulation in (i) ixr1-1 seedlings that had been incubated with supernatants from Col-0 seedlings pretreated with ISX for 12 or 24 hours and (ii) Col-0 seedlings incubated with boiled supernatants from Col-0 seedlings pretreated with Driselase for 12 or 24 hours (fig. S6, A and B). JA accumulation was barely above the detection limit in the seedlings treated with the different supernatants (fig. S6C). With respect to SA accumulation, only minor changes were detected compared to mock-treated samples (fig. S6D). These results suggested that ISX and Driselase treatments do not cause the release of a factor into the medium that is capable of inducing JA and SA accumulation. Together, the osmosensitivity and similarities in seedling responses to different types of CWD (enzymatic versus ISX) suggest that different causes of CWD may stimulate cells similarly (or even in the same way), which, in turn, activates the same cellular responses.

    Mechanical and hypo-osmotic stress sensors mediate certain CWD responses

    The phenotypic data suggested that osmosensitive processes are an important element of the mechanism mediating CWD responses. Therefore, we investigated whether genes implicated in the perception of mechanical [MCA1; MECHANOSENSITIVE CHANNEL OF SMALL CONDUCTANCE (MSCS-LIKE) 4 (MSL4), MSL5, MSL6, MSL9, and MSL10], hypo-osmotic (MCA1, MSL2, and MSL3), and hyperosmotic stresses [ARABIDOPSIS HISTIDINE KINASE1 (AHK1), AHK2, AHK3, and AHK4] were involved in CWI maintenance (17). We used plants harboring mutations in these genes and also included the1-4 mutants in this analysis because the1-4 has been described as a gain-of-function allele that affects the cellular response to the inhibition of cellulose biosynthesis (20, 60). This allowed us to test whether any stimulus perceived by THE1 was also sensitive to osmoticum and to place osmosensitive responses upstream or downstream of THE1-mediated signaling. We treated mutant seedlings with ISX, sorbitol, or a combination of ISX plus sorbitol and measured JA accumulation in whole seedlings and lignification at the root tip (fig. S7, A and B). JA and lignin were selected for this analysis because they enabled us to assess two qualitatively different responses (phytohormone production and cell wall metabolism). For these experiments, we used only ISX because the analysis above had shown that JA accumulation and lignin production are activated similarly by ISX and Driselase in an osmosensitive manner. Only mca1 and msl2 msl3 seedlings exhibited reduced JA accumulation upon ISX treatment compared to the corresponding wild-type (Col-0 or Ws-2) controls (fig. S7A). ISX induced lignin deposition in all mutants and wild-type plants that were tested, but cotreatment with sorbitol reduced ISX-induced lignin accumulation (fig. S7B). Lignin deposition was reduced in mca1 and enhanced in msl4/5/6/9/10 (plants in which MSL4, MSL5, MSL6, MSL9, and MSL10 were all mutated), ahk1, and ahk2 ahk3 seedlings compared to the corresponding controls (Fig. 2A). These results confirm the requirement of MCA1 for ISX-induced JA and lignin accumulation (23) and indicate that MSL2 MSL3 are required for JA accumulation, whereas MSL4/5/6/9/10, AHK1, and AHK2 AHK3 only affect ISX-induced lignin production. In all genotypes examined (including the1-4), sorbitol cotreatments still reduced ISX-induced lignin and JA accumulation. This suggests that the effects of the sorbitol treatment could be due to turgor equilibration, illustrated by the shape changes in ISX-treated root epidermal cells [fig. S1A and previously reported in (61)], and would therefore not require any of the sensors tested (62). Turgor manipulation affects all the phenotypic effects of CWD that we examined, whereas supernatants from seedlings that had previously experienced CWD did not induce phytohormone production, the most sensitive readout of the response to CWD. This suggests that turgor-sensitive, nonsecreted stimuli may activate CWD responses. The substantial accumulation of JA we observed in the1-4 seedlings supports the hypothesis that the plasma membrane–localized RLK THE1 is involved in perception of these turgor-sensitive stimuli. This suggests that the stimuli indicating compromised CWI may consist of cell wall–bound epitopes that change conformation. Alternatively, mechanical distortion or displacement of the plasma membrane against the cell wall upon CWD, similar to the processes activating the CWI maintenance mechanism in S. cerevisiae, is conceivable as stimuli (51).

    Fig. 2 Phenotypic clustering identifies groups of genes involved in CWD responses.

    Quantification of (A) root tip lignification, (B) JA, and (C) SA in the indicated mutant seedlings after treatment with ISX. Values represent means with error bars indicating SD and are expressed relative to the appropriate wild-type control (Col-0 or Ws-2, depending on the genetic background of the mutant strain) from a representative experiment (dashed line). n ≥ 10 (A) and n = 4 (B and C); asterisks indicate statistically significant differences between the mutant and wild type (Student’s t test, *P < 0.05). Mutant lines are organized in functional groups (RLKs, CrRLK1Ls, AHKs, and Ion channels), and individual genotypes are described in detail in table S2. (D) Hierarchical clustering of mutant phenotypes assigning functions in CWI maintenance to candidate genes based on their responses to ISX. Mutant phenotype data from (A) to (C) and fig. S9F [root growth inhibition (RGI)] were normalized to wild-type controls and log2-transformed before average linkage clustering. Blue color indicates reduced ISX responses, and red color indicates increased ISX responses compared to wild type.

    Phenotypic clustering identifies a core group of RLKs and ion channels mediating CWI maintenance

    Genes required for cell elongation, fertilization, and immunity have been implicated in CWI maintenance (3, 10, 19, 63). To gain further insight into the molecular mode of action of CWI maintenance and to establish which of the candidate genes are required and assess their relative importance in the process, we investigated knockout or gain-of-function alleles for 15 RLKs and 1 RLP [THE1, CURVY 1 (CVY1), FER, HERCULES RECEPTOR KINASE 1 (HERK1), HERK2, ERULUS (ERU), WAK2, FEI1, FEI2, MIK2, BAK1, BAK1-LIKE 1 (BKK1), PEPR1, PEPR2, BIK1, and RECEPTOR-LIKE PROTEIN 44 (RLP44)]. The specific alleles of each gene, including a T-DNA insertion allele of WAK2 that we designated as WAK2-12 (fig. S8, A to C), are noted in the figures and summarized in table S2. We measured JA and SA accumulation in mock- and ISX-treated seedlings of these genotypes, as well as in the osmosensing and mechanosensing ahk1, ahk2 and ahk3 (ahk2/3), mca1, msl2 and msl3 (msl2/3), and msl4/5/6/9/10 mutants and in the ISX-resistant ixr1 mutant (Fig. 2, B and C, and fig. S9, A to D). JA and SA accumulation was similar to the corresponding wild-type controls in all mock-treated genotypes with the exception of fer-5 seedlings, which already exhibited increased JA and SA accumulation in the mock-treated samples, in line with the multifunctional nature of FER (fig. S9, A to D) (9, 64). Moreover, ISX-induced JA and SA accumulation was strongly increased in fer-5 compared to wild type, suggesting that FER is not essential for perception of ISX-induced CWD (fig. S9, B and D). We also investigated root growth and ISX resistance, which could potentially distort the analyses performed here, in each genotype and found no substantial deviations from wild type, with the exception of bak1-5 seedlings showing somewhat shorter roots than wild-type seedlings and irx1-1 seedlings being resistant to ISX, as expected. (fig. S9, E and F). We quantified lignin deposition in the root tip area using an image analysis–based approach to generate quantitative data that could be normalized and used for subsequent hierarchical clustering (Fig. 2A). The quantitative data for JA, SA, and lignin accumulation were integrated through hierarchical clustering to generate a global, standardized overview, allowing assessment of both relative importance and functions of individual candidates in CWI maintenance (Fig. 2D). Data for fer-5 were not included in the hierarchical clustering to avoid distortion during data integration due to the increased amounts of phytohormones in mock-treated seedlings (fig. S9, A and C). The results showed that knockouts in five PTI signaling elements (BAK1, BKK1, BIK1, PEPR1, and PEPR2) enhanced JA and SA accumulation in response to ISX treatment. Whereas the WAK2cTAP dominant-active allele exhibited increased JA accumulation, wak2 seedlings showed only a slight and statistically insignificant reduction in JA accumulation, which might be caused by redundancy within this gene family (8). In parallel, fei2 and mik2 seedlings exhibited significant reductions in the CWD responses examined, implicating (in the case of FEI2) or confirming [in the case of MIK2; (25)] their involvement in CWI maintenance. Seedlings in which the CrRLK1L family members CVY1, HERK1, and HERK2 had been knocked out exhibited enhanced hormone responses, whereas eru seedlings were not strongly affected, and the1-1 seedlings exhibited reduced responses, implying functional divergence within the CrRLK1L family. Loss of RLP44, which is involved in cell wall–mediated activation of brassinosteroid signaling (63), did not affect the responses analyzed, suggesting that RLP44 is not required for responses to ISX-induced CWD. In summary, the hierarchical clustering showed that among the genes we tested, MIK2, MCA1, MSL2/3, FEI2, and THE1 are the most important ones for activation of ISX-induced CWD responses. Several of these proteins have been implicated in turgor perception and mechanoperception and are located in the plasma membrane or plastid envelope, both of which are subcellular compartments that are particularly sensitive to changes in turgor and mechanical stimuli (19, 65).

    THE1 is a key signaling element mediating CWD- but not PAMP-induced responses

    We performed a genetic analysis to establish whether THE1, MCA1, and FEI2 are part of the same or different signaling cascade, using both a THE1 loss-of-function (the1-1) and a gain-of-function (the1-4) allele. We generated the mca1 fei2, the1-1 mca1, the1-1 fei2, the1-4 mca1, and the1-4 fei2 double mutants and measured the accumulation of JA, SA, and lignin in these mutant seedlings after mock and ISX treatments. JA, SA, and lignin phenotypes in mca1 fei2, the1-1 mca1, and the1-1 fei2 seedlings were not additive, but fei2 was epistatic to mca1, and the1-1 was epistatic to both mca1 and fei2 (Fig. 3, A to C). Next, we compared responses in the1-4 mca1 and the1-4 fei2 seedlings to the1-4 alone. JA and SA accumulation in the double mutants was reduced compared to the1-4 and similar to Col-0, whereas relative lignification was only reduced in the1-4 mca1 (Fig. 3, D to F). These results suggested that MCA1 and FEI2 are both required for hormone signaling downstream of THE1, but only MCA1 is required for THE1-dependent lignification.

    Fig. 3 THE1 functions upstream of MCA1 and FEI2 and promotes responses to different types of CWD.

    Quantification of (A) root tip lignification, (B) JA, and (C) SA after ISX treatment in wild-type (Col-0) and mutant seedlings carrying the indicated loss-of-function mutations. Quantification of (D) root tip lignification, (E) JA, and (F) SA after ISX treatment in Col-0 and mutant seedlings carrying the gain-of-function allele the1-4 or the1-4 in combination with the loss-of-function allele mca1 or fei2. Quantification of (G) JA and (H) SA in Col-0, the1-1 (loss-of-function), and the1-4 (gain-of-function) seedlings treated with boiled Driselase (bDri) or Driselase (Dri). n ≥ 17 (lignin) and n = 4 (JA and SA). Letters a to e (A to F) indicate statistically significant differences between genotypes according to one-way ANOVA and Tukey’s HSD test (α = 0.05). Asterisks (G and H) indicate statistically significant differences compared to the Col-0 control (Student’s t test, *P < 0.05; **P < 0.01; ***P < 0.001).

    Phenotypic clustering and genetic analyses confirmed THE1 as a key regulatory component in CWI maintenance in response to ISX treatment. To determine whether THE1 was also required for Driselase-induced CWD, we quantified JA and SA accumulation in the1-1 loss-of-function and the1-4 gain-of-function seedlings treated with boiled or active Driselase. SA content was slightly increased in the1-4 controls treated with boiled Driselase compared to Col-0 seedlings, but JA content was similar in all genotypes (Fig. 3, G and H). JA accumulation was reduced in the1-1 and enhanced in the1-4 seedlings upon treatment with active Driselase compared to Col-0 seedlings (Fig. 3G). SA amounts were increased similarly in Col-0 and the1-4 seedlings compared to boiled Driselase controls and even further increased in the1-1 (Fig. 3H). These results suggest that a THE1-dependent mechanism controls JA accumulation in response to both Driselase- and ISX-induced CWD but indicate that additional factors control SA accumulation in Driselase-treated seedlings.

    THE1 is critical for CWI signaling and also involved in pathogen resistance (25). Therefore, it is conceivable that THE1 might also be involved in PTI. We tested this by treating the1-1 and the1-4 seedlings with flg22 and measuring subsequently SA accumulation (fig. S10). SA accumulated similarly in Col-0, the1-1, and the1-4 seedlings. In summary, these results suggest that THE1 acts upstream of MCA1 and FEI2, is required for both ISX and Driselase-induced JA production, but is not required for PTI, implying that THE1 is specifically involved in CWI signaling.

    AtPEP1 and AtPEP3 repress CWD-induced phytohormone production

    To identify transcriptionally regulated elements of the CWI maintenance mechanism, Col-0 seedlings were mock- or ISX-treated and analyzed by RNA sequencing (RNA-seq) using mock- or ISX-treated ixr1-1 seedlings as controls. In Col-0 seedlings treated with ISX for 1 hour, 109 transcripts exhibited statistically significant differences from mock-treated controls (data file S1). Although mock-treated ixr1-1 seedlings exhibited differences compared to mock-treated Col-0 at the transcriptome level, none of the ISX-regulated transcripts in Col-0 were differentially expressed in ISX-treated ixr1-1 seedlings (data file S1). Gene Ontology (GO) enrichment analysis detected an overrepresentation of genes implicated in phytohormone-dependent stress responses in ISX-treated Col-0 seedlings (table S1). Among the differentially expressed transcripts were PROPEP1, PROPEP2, PROPEP3, and PROPEP4, which encode the precursors of the signaling peptides AtPep1 to AtPep4 (fig. S11A) (43). PEPR1 has been shown to bind AtPep1 to AtPep4, whereas PEPR2 binds only AtPep1 and AtPep2 (43). This observation was intriguing given that pepr1 and pepr2 seedlings exhibit enhanced JA accumulation upon ISX treatment (Fig. 2B). Gene expression analysis by qRT-PCR showed that PROPEP1 and PROPEP3 expression were particularly strongly induced by ISX (Fig. 4A). Time course expression analysis of PROPEP1 and PROPEP3 detected increases in expression over time in ISX-treated seedlings, suggesting that AtPep1 and AtPep3 might accumulate in response to ISX treatment (Fig. 4B). Expression of PROPEP1 and PROPEP3 was still increased in ISX-treated the1-1 seedlings, indicating that their induction was independent of THE1-mediated processes (Fig. 4C). AtPep1 enhances JA, SA, and ethylene accumulation in response to wounding (66). To investigate whether AtPep1 also enhanced CWD responses, Col-0 seedlings were treated with different concentrations of AtPep1 alone or in combination with ISX before JA, SA, and lignin accumulation were measured. AtPep1 treatments alone did not induce JA and SA accumulation (Fig. 4, D and E). Seedlings cotreated with ISX and AtPep1 exhibited reductions in ISX-induced JA and SA accumulation in a manner that depended on the concentration of AtPep1 (Fig. 4, D and E). AtPep1 induced lignin deposition in Col-0 seedling roots in a distinctly different pattern than ISX treatment did, whereas lignin deposition seemed to be additive in cotreated root tips compared to roots treated with either AtPep1 or ISX alone (fig. S11B).

    Fig. 4 ISX induces PROPEP expression, and AtPep1 and AtPep3 repress responses to CWD.

    (A) Relative PROPEP1, PROPEP2, PROPEP3, and PROPEP4 expression determined by qRT-PCR in Col-0 seedlings treated with DMSO (mock) or ISX for 1 hour. (B) PROPEP1 and PROPEP3 expression in Col-0 seedlings at the indicated time points. (C) PROPEP1 and PROPEP3 expression in Col-0 and the1-1 seedlings after 1 hour of mock or ISX treatment. Quantification of (D) JA and (E) SA in Col-0 seedlings after cotreatment with either mock conditions or ISX plus 0, 1, 10, or 100 nM AtPep1. Quantification of (F) JA and (G) SA in Col-0, pepr1, pepr2, and pepr1 pepr2 seedlings after cotreatment with either mock conditions or ISX plus 10 nM AtPep1. (H) Root tip lignification in Col-0, pepr1, pepr2, and pepr1 pepr2 seedlings after cotreatment with either mock conditions or ISX plus 10 nM AtPep1 was visualized by phloroglucinol staining. Scale bar, 200 μm. Quantification of (I) JA and (J) SA in Col-0 seedlings after cotreatment with either mock conditions or ISX plus 0 or 10 nM AtPep3. All values represent means with error bars indicating SD. n = 3 (A to C, I, and J), n = 4 (D to G), and n ≥ 5 (H). Asterisks (A to C) indicate statistically significant differences compared to mock-treated controls (Student’s t test, *P < 0.05). Letters a to e (D to J) indicate statistically significant differences between treatments of each genotype according to one-way ANOVA and Tukey’s HSD test (α = 0.05). (K) Seedlings expressing PROPEP3-Venus were mock- or ISX-treated for 24 hours. Proteins in the growth medium were immunoprecipitated, separated by SDS–polyacrylamide gel electrophoresis (PAGE), and stained with silver nitrate. The expected mass of PROPEP3-Venus is 37 kDa, and the expected mass of Venus alone is 27 kDa. n = 2. (L) Numbers of unique PROPEP3-Venus peptides after trypsin digest identified by LC-MS/MS from silver-stained bands at around 25 and 37 kDa. FDR, false discovery rate.

    To exclude indirect effects and determine whether the observed effects on AtPep1 were mediated by the established AtPep1 receptors, the experiments were repeated with Col-0, pepr1, pepr2, and pepr1 pepr2 seedlings. ISX-induced JA and SA accumulation was reduced in pepr1 and pepr2 seedlings upon cotreatment with ISX and AtPep1, similarly to the Col-0 seedlings (Fig. 4, F and G). However, this was not the case in pepr1 pepr2 seedlings, in which cotreatment with AtPep1 did not counteract ISX-induced accumulation of JA and SA, suggesting that AtPep1 can inhibit ISX-induced phytohormone production through either PEPR1 or PEPR2. Analysis of lignin deposition in seedlings treated with AtPep1, ISX, or both showed that PEPR2 is essential for AtPep1-induced lignin deposition, but PEPR1 is not (Fig. 4H). This provided further support for differences between PEPR1 and PEPR2 with respect to signaling activities. We also investigated AtPep3, which binds to PEPR1 but not to PEPR2, and found that AtPep3 cotreatment with ISX had similar effects as AtPep1 cotreatment on JA and SA accumulation in Col-0 seedlings (Fig. 4, I and J). However, AtPep3 did not induce lignin production as did AtPep1 (fig. S11C), which can be explained by the inability of AtPep3 to bind to PEPR2 (42). Because the fusion protein PROPEP3-Venus is secreted upon treatment with AtPep2 (67), we tested whether ISX treatment also induced PROPEP3-Venus secretion. Seedlings stably expressing pPROPEP3::PROPEP3-Venus were mock- or ISX-treated, and we immunoprecipitated the fusion protein from the growth medium (68). On a silver-stained polyacrylamide gel containing the protein preparations from the growth medium, bands of around 25 kDa were visible in preparations from both mock- and ISX-treated samples, consistent with the fusion protein being processed after secretion into the apoplast to release Venus alone (27 kDa) (Fig. 4K). A band corresponding to the size of the full-length fusion protein (PROPEP3-Venus, 37 kDa) was only detected in the preparation from ISX-treated seedlings. Subjecting the proteins in these bands to peptide mass fingerprinting by liquid chromatography–coupled tandem mass spectrometry (LC-MS/MS) identified unique peptides corresponding to PROPEP3 and the VENUS tag with high confidence only in growth medium derived from ISX-treated seedlings (Fig. 4L and data file S2). These results suggest that PROPEP3 is secreted from seedlings in response to ISX treatment.

    Together, our data suggest that CWD induces the production of AtPep1 and AtPep3 through a mechanism that is independent of THE1 and seems to be regulated at the transcriptional level through controlled expression of PROPEP1 and PROPEP3. The AtPep1 and AtPep3 signaling process via PEPR1 and PEPR2 is redundantly organized because only pepr1 pepr2 seedlings are unresponsive to AtPep1 treatment. AtPep1 and AtPep3 seem to act as inhibitors of phytohormone accumulation in response to CWD, whereas they have been previously described exclusively as enhancers of PTI responses. These results suggest that the specific activities of AtPep1 and AtPep3 are context-dependent.

    DISCUSSION

    Here, we have shown that two different types of CWD result in similar, osmosensitive responses in different cell types possible through a nonmobile stimulus, such as an alteration in a cell wall component, structure, or mechanical properties. We identified a small group of molecular components, most of which are involved in the perception of mechanical or hypo-osmotic stress, that mediate both local (deposition of lignin) and systemic (phytohormone accumulation) responses to CWD. Simultaneously, we observed that loss of PTI signaling elements, such as BAK1, BIK1, BKK1, PEPR1, and PEPR2, enhanced the responses to CWD. We showed that THE1, MCA1, and FEI2 belong to the same signaling cascade and that THE1 was involved in mediating responses to both Driselase and ISX-induced CWD but not PTI-associated SA accumulation. We found that CWD induced PROPEP1 and PROPEP3 expression in a THE1-independent manner and that a PROPEP3-VENUS fusion protein is released into the growth medium from seedlings in response to ISX treatment. Application of AtPep1 and AtPep3 repressed CWD-induced JA and SA accumulation in a concentration-dependent manner, and repression by AtPep1 depended on the activity of PEPR1 and PEPR2. These results provide insights into the early events during CWD perception and the mechanisms regulating the cellular and systemic responses.

    ISX and Driselase treatments resulted in similar responses in seedlings. Experiments with the individual cell wall–degrading enzymes found in Driselase and combinations thereof showed that pectinase and cellulase together caused overall the greatest JA accumulation, whereas SA amounts were lower than in seedlings treated with pectinase alone. Pectinase may increase the accessibility of cellulose to cellulase, thus facilitating the breakdown of this load-bearing cell wall component and perhaps explaining the similarities in the observed responses to ISX, Driselase, and combined pectinase plus cellulase treatments. Sorbitol cotreatments dampened all the responses to both enzyme- and ISX-induced CWD, suggesting that CWD responses are induced by a stimulus that is sensitive to turgor pressure. Treatments with supernatants derived from seedlings exposed to ISX or Driselase induced neither JA nor SA accumulation in a manner similar to Driselase and ISX treatments, suggesting that the stimulus activating the CWD responses is not mobile. All the genes that we identified through phenotypic clustering as being required for responses to CWD have been implicated in signal transduction or the perception of hypo-osmotic, mechanical, or CWD (9, 19, 65, 69). This provides further support that the initial stimulus, indicating that CWD has occurred, could be physical (mechanical). Together, these observations suggest that CWD could result in distortion or displacement of the plasma membrane relative to the cell wall possibly caused by changes in the surface tension of the wall itself due to weakening of the load-bearing cellulose framework. These changes, in turn, could be detected by the CWI maintenance mechanism and lead to the observed responses.

    CWD in vivo is often caused by developmental processes, such as cell elongation, abiotic stressors (such as drought or cold temperature), or a pathogen breaking down the cell wall as part of the infection process (7, 70). Each of these different sources of CWD requires specialized, adaptive responses. The data presented here suggest that CWI and PTI signaling may coordinately contribute to these adaptive responses. If plants experience CWD, initially both CWI and AtPep-dependent PTI signaling seem to be activated independently (Fig. 5A). Induction of PROPEP1 and PROPEP3 expression leads to the release of PROPEPs, which are probably processed in the apoplast to generate AtPeps that are perceived by the receptors PEPR1 and PEPR2 and contribute to increased PTI responses (Fig. 5A, red elements). In parallel, CWD is perceived separately through the CWI maintenance mechanism, which enables plant cells apparently to detect mechanical- or osmotic-induced physical damage to their cell walls or the consequences thereof (Fig. 5A, blue elements). If CWD is derived from developmental processes or abiotic stress, activation of the PROPEPs might not be further enhanced because PAMPs are absent, and the responses would consequently be mainly mediated by the CWI mechanism. If CWD is derived from a cell wall–degrading pathogen, PROPEP activation would be enhanced by the simultaneous presence of PAMPs (Fig. 5A). This leads, on the one hand, to increased activation of PTI-controlled, targeted defense responses and, on the other hand, to active repression of CWI-controlled responses through PEPR1 and PEPR2. If PTI is impaired—or if PEPR1 and PEPR2 are inactive—activation of the tailored defense responses is not enhanced, but the CWI-controlled responses are not repressed (Fig. 5B). This means that basal defense responses (exemplified here by JA and SA accumulation) controlled by the CWI maintenance mechanism are enhanced to compensate for a reduction of PTI-controlled responses.

    Fig. 5 Model of stress response integration through CWI and PTI signaling.

    (A) Responses to CWD caused by inhibition of cellulose biosynthesis (ISX) or enzymatic cell wall degradation (Driselase) in Arabidopsis depend on the RLK THE1. THE1 acts upstream of the ion channel MCA1 and the RLK FEI2 to stimulate the CWI maintenance system. Independently, through an unknown mechanism, ISX treatment induces the expression of PROPEP1 and PROPEP3 (PROPEP1/3) and secretion of PROPEP3. Processing of PROPEP1 and PROPEP3 generates the host defense peptides AtPep1 and AtPep3 (AtPep1/3), which are DAMPs that are also induced by pathogen elicitors during PTI. AtPep1/3 repress ISX-induced hormone accumulation through the AtPep receptors PEPR1 and PEPR2, suggesting an AtPep-dependent negative feedback mechanism. AtPep signaling also induces a positive feedback loop that enhances PTI. (B) If PTI-mediated induction of AtPeps or AtPep signaling is impaired, suppression of CWI signaling is alleviated, and the CWI maintenance pathway contributes to stress response induction to a greater extent than does PTI.

    To summarize, the results presented here suggest that PTI and the CWI maintenance mechanism both “detect” CWD in plant cells in different ways and modulate responses in an adaptive manner. Coordination between PTI and CWI maintenance signaling is apparently mediated by AtPep1 and AtPep3, which function in this context as repressors, not enhancers, of CWI signaling. The effects observed in the pepr1 pepr2 double-mutant seedlings suggest that the CWI maintenance mechanism acts as backup system for activating basal defenses in case PTI and activation of the regular defense responses are impaired. Because homologs of THE1, MCA1, and WAK2 have been identified in both monocotyledonous and dicotyledonous plants as well as in more ancient species, the CWI maintenance mechanism—and potentially its interactions with PTI-based defense responses—may be conserved throughout the plant kingdom (28–30).

    MATERIALS AND METHODS Reagents

    All chemicals and enzymes were purchased from Sigma-Aldrich unless stated otherwise.

    Plant growth and treatments

    Wild-type and mutant A. thaliana strains used in this study were ordered from the Nottingham Arabidopsis Stock Centre (http://arabidopsis.info/) or obtained directly from the laboratories previously publishing them. Detailed information is listed in table S2. Seedlings were grown in liquid culture as described (23) with minor modifications. Thirty milligrams of seeds was sterilized by sequential incubation with 70% ethanol and 50% bleach on a rotating mixer for 10 min each and washed three times with sterile water. Seeds were then transferred into 250-ml Erlenmeyer flasks containing 125-ml half-strength Murashige and Skoog growth medium [Murashige and Skoog Basal Medium (2.1 g/liter), MES salt (0.5 g/liter), and 1% sucrose at pH 5.7]. Seedlings were grown under long-day conditions (16-hour light, 22°C and 8-hour dark, 18°C) at a photon flux density of 150 μmol m−2 s−1 on a IKA KS 501 flask shaker at a constant speed of 130 rpm.

    For all experiments, seedlings were grown for 6 days before treatment. The following products were used for treatments at the indicated final concentrations throughout the paper, unless stated otherwise: ISX (600 nM; DMSO), mock (DMSO), Driselase (0.03%, w/v; D8037, Sigma-Aldrich), cellulase (0.09%, w/v; C8001, Duchefa), pectinase (0.09%, w/v; 17389, Sigma-Aldrich), xylanase (0.09%, w/v; X2753, Sigma-Aldrich), and sorbitol (300 mM). For heat inactivation, enzymes were boiled for 10 min. Supernatants from treated Col-0 cultures were incubated with ixr1-1 seedlings (DMSO, ISX, and ISX + S) or boiled for 10 min and incubated with Col-0 seedlings (DMSO + S, bDri, bDri + S, Dri, and Dri + S). AtPep1 (ATKVKAKQRGKEKVSSGRPGQHN), AtPep3 (EIKARGKNKTKPTPSSGKGGKHN), and flg22 (QRLSTGSRINSAKDDAAGLQIA) peptides were obtained from Peptron and dissolved in sterile water.

    Confocal laser scanning microscopy

    WAVE 131Y (52) seedlings used to investigate structural changes in root and cotyledon cells after CWD were placed on microscopy slides, covered with the same medium used for the treatment, and imaged with a Leica SP8 confocal laser scanning microscope. Four Z-stacks were taken for each of the conditions analyzed using HC PL APO 10×/0.40 DRY objective [excitation, 514 nm; BA (barrier filter), 525 to 535], 0.7–airy unit pinhole, and 700-V gain. Z-stacks were transformed in two-dimensional images by using the maximum intensity projections (0, threshold) function on LAS X software. To highlight the cell outlines, Z-projection images were transformed in grayscale using GIMP v2.8.22 and presented as insets.

    Phytohormone analysis

    The JA and SA contents of seedlings were analyzed as described in (71), with minor modifications. Seedlings were sampled at 7 hours after treatment, flash-frozen in liquid nitrogen, and freeze-dried for 24 hours. Aliquots each containing 6 to 7 mg of freeze-dried seedlings were ground with 5-mm stainless steel beads in a Qiagen TissueLyser II for 2 min at 25 Hz. Shaking was repeated after the addition of 400-μl extraction buffer (10% methanol and 1% acetic acid) with internal standards (10 ng of jasmonic-d5 acid and 28 ng of salicylic-d4 acid; CDN Isotopes) before samples were incubated on ice for 30 min and centrifuged for 10 min at 16,000g and 4°C. Supernatants were transferred into fresh tubes, and the pellets were reextracted with 400-μl extraction buffer without internal standards. Supernatants were combined and centrifuged three times to remove all debris before LC-MS/MS analysis. An extraction control not containing plant material was treated equally to the plant samples. Chromatographic separation was carried out on a Shimadzu UFLC XR, equipped with a Waters Cortecs C18 column (2.7 μm; 2.1 × 100 mm). The solvent gradient [acetonitrile (ACN)/water with 0.1 % formic acid each] was adapted to a total run time of 7 min: 0 to 4 min, 20 to 95% ACN; 4 to 5 min, 95% ACN; 5 to 7 min, 95 to 20% ACN; flow rate, 0.4 ml/min. For hormone identification and quantification, an AB SCIEX Triple Quad 5500 system was used. Mass transitions were as follows: JA 209 > 59, D5-JA 214 > 62, SA 137 > 93, D4-SA 141 > 97.

    Callose analysis

    Seedlings were sampled 24 hours after treatment and placed in 70% (v/v) ethanol. For callose staining, samples were incubated in 0.07 M sodium phosphate buffer (pH 9) for 30 min and in 0.005% (w/v) aniline blue [in 0.07 M sodium phosphate buffer (pH 9)] for 60 min. Samples were washed with water, mounted in 50% (v/v) glycerol, and analyzed on a Nikon Eclipse E800 microscope using a UV-2A filter (excitation, 330 to 380 nm; dichroic mirror, 400 nm; BA, 420 nm). Images were taken at ×10 magnification, and callose depositions were quantified using ImageJ software.

    Lignin analysis

    Lignification was investigated 12 hours (root tips) and 24 hours (cotyledons) after the start of treatments. Lignin was detected with phloroglucinol-HCl as described (23). Seedlings were photographed using a Zeiss Axio Zoom.V16 stereomicroscope. To assess the extent of lignin production in root tips, phloroglucinol-stained areas and the total root area imaged were quantified using ImageJ (the same root length was maintained in all images taken). The relative lignified area was plotted as fold change compared to wild-type root tips.

    Cell death analysis

    Seedlings were sampled after 24 hours of treatment and incubated in trypan blue staining solution (0.025% trypan blue, 25% phenol, dissolved in equal volumes of lactic acid, glycerol, and water) for 6 hours at room temperature. Samples were destained in chloral hydrate overnight and transferred into 60% glycerol before microscopy. Images of the cotyledons were obtained with a Zeiss Axio Zoom.V16 stereomicroscope. The percentages of trypan blue–stained areas were quantified from cotyledons using ImageJ color thresholding.

    Root growth measurements

    Absolute root lengths were measured immediately before ISX treatment (0 hours) to examine root growth phenotypes and 24 hours after start of treatment to determine ISX-dependent RGI. For calculation of %RGI, the following formula was applied: [1 − (ISX 24 hours − ISX 0 hours)/(mock 24 hours − mock 0 hours)]*100.

    Hierarchical cluster analysis

    Hierarchical clustering of ISX-dependent phenotypes was performed with Cluster 3.0 using the C Clustering Library v1.52 (72). All data from mutant seedlings were normalized to their corresponding wild-type control. Log2-transformed data were then used for average linkage clustering with an uncentered correlation similarity metric. Results were depicted using Java TreeView v1.1.6r4 and color-coded blue (less than in wild type) or red (more than in wild type) (73).

    Genotyping the WAK2 T-DNA insertion

    Seeds were sown on a six-well plate and grown in 1/2 MS1 for 6 days. Genomic DNA was extracted by grinding the plant material in a 2-ml Eppendorf tube with 5-mm stainless steel beads and 500 μl of extraction buffer [0.5 M NaCl, 50 mM EDTA, 0.1 M tris-HCl (pH 8.0)] in a Qiagen TissueLyser II for 1 min at 25 Hz. The lysate was centrifuged, and 300 μl of supernatant was combined with 300 μl of isopropanol to precipitate the DNA. After centrifugation, the pellet was washed with 70% ethanol and dissolved in 100 μl of Milli-Q water. One microliter of the isolated DNA was used for PCR using Taq polymerase. The PCR program included an initial denaturation at 95°C for 2 min, followed by 30 cycles with 95°C for 30 s, 57°C for 30 s, and 72°C for 1 min with final elongation at 72°C for 2 min. The PCR products were run on 1% agarose gel containing GelRed dye and imaged with a Syngene G:BOX imaging device.

    Quantitative reverse transcription polymerase chain reaction

    Total RNA was isolated using a Spectrum Plant Total RNA kit (Sigma-Aldrich). Two micrograms of total RNA was treated with RQ1 RNase-Free DNase (Promega) and processed with the ImProm-II Reverse Transcription System (Promega) for complementary DNA (cDNA) synthesis. qRT-PCR was performed using a LightCycler 480 SYBR Green I Master (Roche) and primers (table S3) diluted according to the manufacturer’s specifications. Four different reference genes (PP2A, ACT2, UBA1, and GRF2) were examined to identify one exhibiting stable expression during ISX treatment. ACT2 was the most stable and used in all experiments as a reference.

    RNA-seq analysis

    Total RNA was extracted using a Spectrum Plant Total RNA kit (Sigma-Aldrich). RNA concentration was measured using a Qubit RNA HS Assay kit (Thermo Fisher Scientific), and the integrity of the RNA was assessed using an Agilent RNA 6000 Pico kit. RNA-seq libraries were prepared using a TruSeq Stranded mRNA kit (Illumina) according to the manufacturer’s instructions. Total RNA (500 ng) was used as starting material.

    First, index barcodes were ligated for identification of individual samples. mRNA purification, fragmentation, and cDNA synthesis were performed as described in (74). Exonuclease/polymerase was used to produce blunted overhangs. Illumina SR adapter oligonucleotides were ligated to the cDNA after 3′ end adenylation. DNA fragments were enriched by 15 cycles of PCR. The libraries were purified using the AMPure XP (Beckman Coulter), quantitated by qPCR using a KAPA Library Quantification kit (Kapa Biosystems), and validated using an Agilent High Sensitivity DNA kit on a Bioanalyzer. The size range of the DNA fragments was measured to be in the range of 200 to 700 base pairs (bp) and peaked around 296 bp. Libraries were normalized and pooled to 2.2 pM and subjected to clustering on NextSeq 500 high-output flow cells. Finally, single-read sequencing was performed for 75-bp read lengths on a NextSeq 500 instrument (Illumina) according to the manufacturer’s instructions. Base calling has been performed on the NS500 instrument by Illumina RTA v2.4.6. FASTQ files were generated using bcl2fastq2 Conversion Software v1.8.4. Each FASTQ file was subjected to quality control through FastQC v11.1 before technical replicates were combined, and an average of 13.1 million reads was produced for each library. The reads were then aligned to the A. thaliana genome (Ensembl v82) with STAR v2.4.1 in two-pass mode. On average, 96.2% of the reads aligned to the genome. The reads that aligned uniquely to the genome were aggregated into gene counts with featureCounts v1.4.6 using the genome annotations defined in Ensembl v82. Of the 32,000 genes defined in the gene model, a total of 20,750 genes were left for analysis after filtering out genes with a CPM (counts per million) value less than 1 in two or more samples.

    The filtered gene count table was used as input to the Voom method of the limma R package v3.26.9 for differential expression (75). The samples were normalized using the TMM (trimmed mean of M values normalization) method before a linear model was defined (76). Differential expression between groups was tested by empirical Bayesian moderated t tests, and P values were corrected for multiple testing by the Benjamini-Hochberg FDR adjustment. Statistical significance of pairwise comparisons was determined using a Student’s t test. Genes with significantly altered expression after 1 hour of ISX treatment (data file S1) were analyzed for GO enrichment using the PANTHER Overrepresentation Test (release 15 July 2016) and the GO Ontology database (release 28 February 2017) on http://geneontology.org/. Results were filtered by P < 0.05 after Bonferroni correction for multiple testing. Data generated in the transcriptomics experiments are available under the following Gene Expression Omnibus (GEO) submission ID: GSE109613.

    Extracellular PROPEP3-Venus assay and peptide mass fingerprinting

    Seedlings stably expressing pPROPEP3::PROPEP3-Venus were grown for 6 days before mock and ISX treatments (68). After 24 hours, the growth medium was filtered through sterile Miracloth and pH-adjusted to 7.5 with KOH. The number of seedlings per treatment was counted. Protease inhibitor cocktail (P9599, Sigma-Aldrich) and phosphatase inhibitor cocktail 1 (P2850, Sigma-Aldrich) were added to the medium. GFP-Trap agarose beads (ChromoTek) were equilibrated according to the manufacturer’s instructions, and 50 μl of bead slurry was added to 2-ml medium. The suspension was tumbled end-over-end for 2 hours at 4°C. Beads were recovered by centrifugation and washed as described. Proteins were dissociated from beads by incubating for 10 min at 95°C in 2× Laemmli buffer, and supernatants were separated via 10% acrylamide gel for SDS-PAGE. Proteins were visualized using a Bio-Rad Silver Stain Plus kit according to the manufacturer’s instructions, and the gel was imaged on a Bio-Rad ChemiDoc XRS+ System.

    Gel bands were cut in smaller pieces (3 to 5 mm3) and were destained by incubation for 2 min in 150-μl ProteoSilver Destainer solution mix. The gel pieces were washed with ultrapure water and then shrunk with ACN. They were reduced with dithiothreitol at 56°C, alkylated by iodoacetamide at room temperature in the dark, and—after being washed and shrunk—digested by trypsin at 37°C overnight. Peptides were collected, dried in a vacuum concentrator, and reconstituted in 0.1% formic acid.

    LC-MS/MS analysis was performed on an EASY-nLC 1200 UPLC system interfaced with a Q Exactive HF mass spectrometer via a Nanospray Flex ion source. Peptides were injected onto an Acclaim PepMap100 C18 trap column (75-μm inside diameter, 2 cm long, 3 μm, 100 Å) and further separated on an Acclaim PepMap100 C18 analytical column (75-μm inside diameter, 50 cm long, 2 μm, 100 Å) using a 60-min gradient (40 min, 5 to 40% B; 7 min, 40 to 100% B; 13 min, 100% B; where B is 0.1% formic acid in CH3CN) at a flow of 250 nl/min. Peptides were analyzed in positive ion mode under data-dependent acquisition using the following parameters: electrospray voltage, 1.9 kV; HCD fragmentation with normalized collision energy, 28%. Each MS1 scan [200 to 2000 mass/charge ratio (m/z), profile] was acquired at a resolution of 120,000 full width at half maximum (FWHM) in the Orbitrap analyzer, followed by 15,000 FWHM MS2 scans (1.2 m/z isolation width, centroid) triggered for the 12 most intense ions, with a 15-s dynamic exclusion. Charge exclusion was set to unassigned, 1, and greater than 5.

    Database search was performed in Proteome Discoverer 2.2 using Sequest HT engine against the proteome of A. thaliana at UniProt (UP000006548; 3 September 2017), the amino acid sequence for PROPEP3-Venus, and a list of sequences of usual protein contaminants. The search allowed up to two missed cleavages, precursor mass tolerance was 10 parts per million, and fragment mass tolerance was 0.02 Da. Carbamidomethyl (M +57.021 Da) was set as static modification, and up to four dynamic modifications per peptide were allowed. Possible modifications were as follows: oxidation (H,M,W +15.995 Da) and deamidation (N,Q +0.984 Da). Validation was performed at peptide spectrum match, peptide, and protein level with high confidence set as 1% FDR, and medium confidence representing 5% FDR. The mass spectrometry–derived proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the data set identifier PXD009153 (77).

    Statistical analysis

    Statistical significance was assessed using either Student’s t test or one-way ANOVA, followed by post hoc analysis with Tukey’s HSD test. Statistical details of experiments are specified in the figure legends. Statistically significant differences are indicated by *P < 0.05, **P < 0.01, and ***P < 0.001 for Student’s t test and different letters for one-way ANOVA/Tukey’s HSD test at α = 0.05. All statistical analyses were performed in IBM SPSS Statistics v24.



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